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Aalto University publication series
DOCTORAL DISSERTATIONS 147/2014
Equalization Techniques for
Headphone Listening
Jussi Rämö
A doctoral dissertation completed for the degree of Doctor of
Science (Technology) to be defended, with the permission of the
Aalto University School of Electrical Engineering, at a public
examination held at the lecture hall S1 of the school on 31 October
2014 at 12.
Aalto University
School of Electrical Engineering
Department of Signal Processing and Acoustics
Supervising professor
Prof. Vesa Välimäki
Thesis advisor
Prof. Vesa Välimäki
Preliminary examiners
Dr. Aki Härmä, Philips Research, The Netherlands
Dr. Joshua D. Reiss, Queen Mary University of London, UK
Opponent
Dr. Sean Olive, Harman International, USA
Aalto University publication series
DOCTORAL DISSERTATIONS 147/2014
© Jussi Rämö
ISBN 978-952-60-5878-8
ISBN 978-952-60-5879-5 (pdf)
ISSN-L 1799-4934
ISSN 1799-4934 (printed)
ISSN 1799-4942 (pdf)
http://urn.fi/URN:ISBN:978-952-60-5879-5
Unigrafia Oy
Helsinki 2014
Finland
Abstract
Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi
Author
Jussi Rämö
Name of the doctoral dissertation
Equalization Techniques for Headphone Listening
Publisher School of Electrical Engineering
Unit Department of Signal Processing and Acoustics
Series Aalto University publication series DOCTORAL DISSERTATIONS 147/2014
Field of research Acoustics and Audio Signal Processing
Manuscript submitted 13 February 2014
Date of the defence 31 October 2014
Permission to publish granted (date) 6 August 2014
Language English
Monograph
Article dissertation (summary + original articles)
Abstract
The popularity of headphones has increased rapidly along with digital music and mobile
phones. The environment in which headphones are used has also changed quite dramatically
from silent to noisy, since people are increasingly using their headphones while commuting and
traveling. Ambient noise affects the quality of the perceived music as well as compels people to
listen to the music with higher volume levels.
This dissertation explores headphone listening in the presence of ambient sounds. The
ambient sounds can be either noise or informative sounds, such as speech. The ﬁrst portion of
this work addresses the ﬁrst case, where the ambient sounds are undesirable noise that
deteriorates the headphone listening experience. The second portion tackles the latter case, in
which the ambient sounds are actually informative sounds that the user wants to hear while
wearing headphones, such as in an augmented reality system. Regardless of the nature of the
ambient sounds, the listening experience can be enhanced with the help of equalization.
This work presents a virtual listening test environment for evaluating headphones in the
presence of ambient noise. The simulation of headphones is implemented using digital ﬁlters,
which enables arbitrary music and noise test signals in the listening test. The disturbing effect
of ambient noise is examined with the help of a simulator utilizing an auditory masking model
to simulate the timbre changes in music. Another study utilizes the same principles and
introduces an adaptive equalizer for mitigation of the masking phenomenon. This psychoacoustic audio processing system was shown to retain reasonably low sound pressure levels
while boosting the music, which is highly important from the viewpoint of hearing protection.
Furthermore, two novel hear-through systems are proposed, the ﬁrst of which is a digital
augmented reality headset substituting and improving a previous analog system. The second
system is intended to be worn during loud concerts as a user-controllable hearing protector and
mixer. The main problem in both of the systems is the risk of a comb ﬁltering effect, which can
deteriorate the sound quality. However, it is shown that the comb ﬁltering effect is not
detrimental due to the passive isolation of an in-ear headset.
Finally, an optimization algorithm for high-order graphic equalizer is developed, which
optimizes the order of adjacent band ﬁlters to reduce ripple around the ﬁlter transition bands.
Furthermore, a novel high-precision graphic equalizer is introduced based on parallel secondorder sections. The novel equalization techniques are intended for use not only in headphone
applications, but also in wide range of other audio signal processing applications, which require
highly selective equalizers.
Keywords Acoustic signal processing, audio systems, augmented reality, digital ﬁlters,
psychoacoustics
ISBN (printed) 978-952-60-5878-8
ISBN (pdf) 978-952-60-5879-5
ISSN-L 1799-4934
ISSN (printed) 1799-4934
ISSN (pdf) 1799-4942
Location of publisher Helsinki
Pages 151
Location of printing Helsinki Year 2014
urn http://urn.ﬁ/URN:ISBN:978-952-60-5879-5
Tiivistelmä
Aalto-yliopisto, PL 11000, 00076 Aalto www.aalto.fi
Tekijä
Jussi Rämö
Väitöskirjan nimi
Ekvalisointitekniikoita kuulokekuunteluun
Julkaisija Sähkötekniikan korkeakoulu
Yksikkö Signaalinkäsittelyn ja akustiikan laitos
Sarja Aalto University publication series DOCTORAL DISSERTATIONS 147/2014
Tutkimusala Akustiikka ja äänenkäsittelytekniikka
Käsikirjoituksen pvm 13.02.2014
Julkaisuluvan myöntämispäivä 06.08.2014
Monografia
Väitöspäivä 31.10.2014
Kieli Englanti
Yhdistelmäväitöskirja (yhteenveto-osa + erillisartikkelit)
Tiivistelmä
Digitaalisen musiikin ja matkapuhelimien yleistyminen ovat lisänneet kuulokkeiden suosiota.
Samalla kuulokkeiden tavallinen käyttöympäristö on muuttunut hiljaisesta meluisaksi, sillä
ihmiset käyttävät kuulokkeita liikkuessaan. Taustamelu vaikuttaa havaittuun äänenlaatuun ja
myös pakottaa ihmiset kuuntelemaan musiikkia suuremmalla äänenvoimakkuudella.
Tässä väitöskirjatyössä tutkitaan kuulokekuuntelua tilanteissa, joissa kuullaan myös
ympäristöstä lähtöisin olevia ääniä. Ympäristön äänet voivat olla melua tai informatiivisia
ääniä, kuten puhetta. Tämän työn ensimmäinen osa keskittyy tapaukseen, jossa ympäristön
äänet ovat kuulokekuuntelua häiritsevää melua. Toisessa osassa ympäristön äänet ovat
informatiivisia hyötyääniä, jotka käyttäjä haluaa kuulla vaikka käyttäisikin kuulokkeita, kuten
esim. lisätyn todellisuuden järjestelmässä. Kummassakin tapauksessa
kuulokekuuntelukokemusta voidaan parantaa ekvalisaattorin avulla.
Tässä työssä esitetään virtuaalinen kuuntelukoeympäristö kuulokkeiden arviointiin melussa.
Kuulokkeiden simulointi on toteutettu digitaalisilla suotimilla, jotka mahdollistavat
mielivaltaisten testisignaalien käytön. Taustamelun aiheuttamaa peittoilmiötä tutkitaan
simulaattorin avulla, joka hyödyntää auditiivista peittomallia simuloimaan havaittua musiikkia
melussa. Samoja periaatteita hyödyntämällä on toteutettu myös psykoakustinen adaptiivinen
ekvalisaattori, joka mukautuu kuunneltavaan musiikiin ja ympäristön meluun. Työssä
näytettiin, että ekvalisaattori säilyttää musiikin kohtuullisen äänipainetason, koska se
vahvistaa vain tarvittavia taajuusalueita. Tämä on erittäin tärkeää kuulovaurioiden
ehkäisemisen kannalta.
Työssä esitetään myös kaksi digitaalista läpikuuluvuussovellusta, joista ensimmäinen on
analogisen järjestelmän paranneltu versio. Toinen sovellus on kehitetty konsertteja varten,
joissa tarvitaan kuulonsuojausta. Sovellus mahdollistaa käyttäjäkohtaisen kuulonsuojauksen
ja läpikuultavan musiikin ekvalisoinnin. Molempien sovellusten ongelmana on mahdollinen
kampasuodinilmiö, joka voi heikentää järjestelmän äänenlaatua. Työssä kuitenkin osoitettiin,
että kampasuodinilmiö on hallittavissa tulppakuulokkeiden hyvän vaimennuskyvyn ansiosta.
Lisäksi tässä työssä kehitetään korkea-asteiselle graaﬁselle ekvalisaattorille optimointialgoritmi, joka vähentää ekvalisaattorin vasteen värähtelyä siirtymäkaistoilla. Tässä työssä
kehitettiin myös uusi tarkka graaﬁnen ekvalisaattori, joka perustuu rinnakkaisiin toisen asteen
suotimiin. Uusia ekvalisointitekniikoita voidaan käyttää kuulokesovellusten lisäksi myös
monissa muissa audiosignaalinkäsittelyn sovelluksissa.
Avainsanat Akustinen signaalinkäsittely, audiojärjestelmät, digitaaliset suodattimet, lisätty
todellisuus, psykoakustiikka
ISBN (painettu) 978-952-60-5878-8
ISBN (pdf) 978-952-60-5879-5
ISSN-L 1799-4934
ISSN (painettu) 1799-4934
ISSN (pdf) 1799-4942
Julkaisupaikka Helsinki
Sivumäärä 151
Painopaikka Helsinki
Vuosi 2014
urn http://urn.ﬁ/URN:ISBN:978-952-60-5879-5
Preface
The work presented in this dissertation was carried out at the Department of Signal Processing and Acoustics at Aalto University School of
Electrical Engineering in Espoo, Finland, during the period of September
2009 and October 2014. During this period, I have been lucky to have had
the chance to be involved in various projects with Nokia Gear and Nokia
Research Center. Without these great projects the writing of this thesis
would not have been possible.
I want to express my deepest gratitude to my supervisor Prof. Vesa
Välimäki for all the guidance and support during the writing of this dissertation. Your guidance has been inspiring and invaluable. I am also extremely grateful to my ﬁrst supervisor Prof. Matti Karjalainen who originally believed in me and gave me the chance to work in the acoustics lab
back in 2008.
I wish to thank my co-authors Dr. Miikka Tikander, Mr. Mikko Alanko,
and Prof. Balázs Bank, for all your contributions. We managed to do
pretty cool stuff together. I would also like to thank all the people from
Nokia, with whom I have had the chance to work with during the projects.
I also want to thank Luis Costa for proof-reading my work and constantly
helping me to improve my writing skills.
I would like to thank my pre-examiners Dr. Joshua D. Reiss and Dr. Aki
Härmä, whose comments were valuable in further improving this dissertation. Thank you also to Dr. Sean Olive for agreeing to act as my opponent.
As many have said before, the working environment in the acoustics lab
is absolutely top-notch and I have had the pleasure to work with fantastic colleagues in the lab over the years. I would like to thank my
co-workers: Sami, Julian, Heidi-Maria, Rafael, Stefano, Hannu, Jussi,
Antti, Henkka, Pasi, Ole, Rémi, Jari, Jyri, Heikki T., Fabian, Atakan,
1
Preface
Ville, Magge, Mikkis, Javier, Tapani, Olli, Marko, Ilkka, Tuomo, Teemu,
Okko, Henna, Symeon, Archontis, Julia, Sofoklis, and all the other former
and present people at the acoustics lab I have met during these years.
Of course, I also need to thank all my friends outside the university who
have helped me to maintain the balance between work and free-time.
I am deeply grateful to my parents Arto Rämö and Heli Salmi for always believing and supporting me and showing pride in the things I do.
I cannot thank you enough for that. Your support has been and still is
extremely important to me. Finally, I would like to say special thanks to
my girlfriend Minna for all the support and love you have shown me over
the years.
Espoo, September 18, 2014,
Jussi Rämö
2
Contents
Preface
1
Contents
3
List of Publications
5
Author’s Contribution
7
List of Symbols
9
List of Abbreviations
11
1. Introduction
13
2. Headset Acoustics and Measurements
17
2.1 Headphone Types . . . . . . . . . . . . . . . . . . . . . . . . .
17
2.2 Headset Acoustics . . . . . . . . . . . . . . . . . . . . . . . . .
18
2.3 Ear Canal Simulators . . . . . . . . . . . . . . . . . . . . . . .
20
2.4 Frequency Response Measurements . . . . . . . . . . . . . .
22
2.5 Headphone Equalization . . . . . . . . . . . . . . . . . . . . .
24
2.6 Ambient Noise Isolation . . . . . . . . . . . . . . . . . . . . .
25
2.7 Occlusion Effect . . . . . . . . . . . . . . . . . . . . . . . . . .
27
3. Augmented Reality Audio and Hear-Through Systems
29
3.1 Active Hearing Protection . . . . . . . . . . . . . . . . . . . .
29
3.2 Augmented Reality Audio . . . . . . . . . . . . . . . . . . . .
30
3.2.1 ARA Applications . . . . . . . . . . . . . . . . . . . . .
31
3.3 Comb-Filtering Effect . . . . . . . . . . . . . . . . . . . . . . .
32
4. Auditory Masking
35
4.1 Critical Bands . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
4.2 Estimation of Masking Threshold . . . . . . . . . . . . . . . .
36
3
Contents
4.3 Partial Masking . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Digital Equalizers
38
39
5.1 Equalizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
5.2 Parametric Equalizer Design . . . . . . . . . . . . . . . . . .
41
5.3 High-Order Graphic Equalizer Design . . . . . . . . . . . . .
42
5.4 Parallel Equalizer Design . . . . . . . . . . . . . . . . . . . .
44
5.5 Comparison of the Graphic Equalizers . . . . . . . . . . . . .
46
6. Summary of Publications and Main Results
49
7. Conclusions
53
Bibliography
57
Errata
69
Publications
71
4
List of Publications
This thesis consists of an overview and of the following publications which
are referred to in the text by their Roman numerals.
I J. Rämö and V. Välimäki. Signal Processing Framework for Virtual
Headphone Listening Tests in a Noisy Environment. In Proc. AES 132th
Convention, 6 pages, Budapest, Hungary, April 2012.
II J. Rämö, V. Välimäki, M. Alanko, and M. Tikander. Perceptual Frequency Response Simulator for Music in Noisy Environments. In Proc.
AES 45th Int. Conf., 10 pages, Helsinki, Finland, March 2012.
III J. Rämö, V. Välimäki, and M. Tikander. Perceptual Headphone Equalization for Mitigation of Ambient Noise. In Proc. Int. Conf. Acoustics,
Speech, and Signal Processing, pp. 724–728, Vancouver, Canada, May
2013.
IV J. Rämö and V. Välimäki. Digital Augmented Reality Audio Headset.
Journal of Electrical and Computer Engineering, Vol. 2012, Article ID
457374, 13 pages, September 2012.
V J. Rämö, V. Välimäki, and M. Tikander. Live Sound Equalization and
Attenuation with a Headset. In Proc. AES 51st Int. Conf., 8 pages,
Helsinki, Finland, August 2013.
VI J. Rämö and V. Välimäki. Optimizing a High-Order Graphic Equalizer
for Audio Processing. IEEE Signal Processing Letters, Vol. 21, No. 3,
pp. 301–305, March 2014.
5
List of Publications
VII J. Rämö, V. Välimäki, and B. Bank. High-Precision Parallel Graphic
Equalizer. IEEE/ACM Transactions on Audio, Speech and Language
Processing, Vol. 22, No. 12, pp. 1894–1904, December 2014.
6
Author’s Contribution
Publication I: “Signal Processing Framework for Virtual Headphone
Listening Tests in a Noisy Environment”
The author collaborated in the development of the ﬁlter design algorithms.
He made the measurements and implemented the whole system by himself. He also wrote and presented the entire work.
Publication II: “Perceptual Frequency Response Simulator for Music
in Noisy Environments”
The author conducted the measurements, listening tests, and implementation of the perceptual frequency response simulator in collaboration
with the co-authors. Furthermore, the author wrote the article and presented it at the conference.
Publication III: “Perceptual Headphone Equalization for Mitigation of
Ambient Noise”
The author collaborated in the algorithm development. He implemented
the signal processing algorithms for the perceptual headphone equalizer.
Furthermore, the author wrote and presented the entire paper.
Publication IV: “Digital Augmented Reality Audio Headset”
The author collaborated in the signal processing algorithm development.
He implemented and wrote the work in its entirety except for the writing
of abstract and conclusions.
7
Author’s Contribution
Publication V: “Live Sound Equalization and Attenuation with a
Headset”
The author designed the system in collaboration with the co-authors, based
on the original idea of the second author. The author programmed the
Matlab simulator as well as the real-time demonstrator. Furthermore,
the author wrote the article and presented it at the conference.
Publication VI: “Optimizing a High-Order Graphic Equalizer for
Audio Processing”
The author discovered, designed, and implemented the optimization algorithm and was the main author of the paper.
Publication VII: “High-Precision Parallel Graphic Equalizer”
The author was responsible for coordinating and editing the work based
on the idea of the co-authors. The author wrote the abstract and Sections
I, IV, and V. He also collaborated in the algorithm design and implemented
most of the new Matlab codes and examples as well as provided all the
ﬁgures and tables included in the paper.
8
List of Symbols
A(z)
transfer function of an allpass ﬁlter
a
frequency parameter of the Regalia-Mitra ﬁlter
ac
frequency parameter of the Regalia-Mitra ﬁlter for cut cases
ak,1 , ak,2
denominator coefﬁcients of the parallel equalizer
b
allpass coefﬁcient of the Regalia-Mitra ﬁlter
bk,0 , bk,1
numerator coefﬁcients of the parallel equalizer
B
two-slope spreading function
c
speed of sound
f
frequency in Hertz
fr,open
ﬁrst quarter-wavelength resonance of an open ear canal
fr,closed
ﬁrst quarter-wavelength resonance of a closed ear canal
gd
gain of direct sound
geq
gain of equalized sound
h(t)
impulse response of a system
H(z)
transfer function of a system
K
gain of the Regalia-Mitra ﬁlter
L
delay in samples
LM
sound pressure level of a masker
l
length of the ear canal
leﬀ
effective length of the ear canal
P
number of ﬁlter sections
r
average radius of the ear canal
U
masking energy offset
x(t)
input signal (sine sweep)
y(t)
output signal (recorded sine sweep)
ν
frequency in Bark units
Ω
normalized ﬁlter bandwidth of the Regalia-Mitra ﬁlter
ω0
normalized center frequency of the Regalia-Mitra ﬁlter
9
List of Symbols
10
List of Abbreviations
ANC
active noise control
ARA
augmented reality audio
CPU
central processing unit
DRP
drum reference point
FIR
ﬁnite impulse response
FFT
fast Fourier transform
GPS
Global Positioning System
GPU
graphics processing unit
HRTF
head-related transfer function
IEC
International Electrotechnical Commission
IFFT
inverse fast Fourier transform
IIR
inﬁnite impulse response
ISO
International Organization for Standardization
ITU-T
International Telecommunication Union,
Telecommunication Standardization Sector
LPC
linear prediction coefﬁcients
NIHL
noise-induced hearing loss
PGE
parallel graphic equalizer
SPL
sound pressure level
11
List of Abbreviations
12
1. Introduction
Nowadays many people listen to music through headphones mostly in
noisy environments due to the widespread use of portable music players,
mobile phones, and smartphones. Gartner Inc. reported that worldwide
mobile phone sales to end users totaled 1.75 billion units in 2012, while
the fourth quarter of 2012 realized a record in smartphone sales totaling
over 207 million units, which was more than a 38 percent increase from
the same quarter of 2011 [1]. However, already in the third quarter of
2013, smartphones sales reached over 250 million units, and the forecast
of global mobile phone sales in 2013 is 1.81 billion units [2]. Almost all
mobile phones, including low-priced budged phones, are capable of playing music and typically come with a stereo headset, which can also be
used as a hands-free device (see Figure 1.1). Thus, basically everybody
who owns a mobile phone is a potential headphone user.
Due to the mobile nature of today’s headphone usage, the listening environments have also changed quite dramatically from quiet homes and
ofﬁces to noisy streets and public transportation vehicles. The increased
ambient noise of the mobile listening environments introduces a problem,
since the noise masks the music by reducing its perceived loudness or by
(a)
(b)
(c)
Figure 1.1. Different mobile headsets, where (a) shows a smartphone and an in-ear headset, (b) shows a button-type headset, and (c) shows an on-the-ear headset.
13
Introduction
masking parts of the music completely. This often leads to increased noise
exposure levels, since people try to compensate the masking of the music
by turning up the volume, which can increase the risk of hearing impairment. First, people should use suitable headphones that isolate ambient
sounds well, and secondly, the ‘unmasking’ of the music signal should be
done in a way that the level of the music is increased as little as possible.
Additionally, now that people increasingly use their headphones, it is
necessary to have a hear-through function in the headphones so that communication is easy and enjoyable even without removing the headphones.
This requires built-in microphones and an equalizer to compensate for the
changes that the headphones introduce to our outer ear acoustics. The
ideal hear-through function, which is also the basis in augmented reality
audio (ARA) [3], is to have completely transparent headphones that do
not alter the timbre of the incoming sounds at all.
This work considers how we can devise and assess improved signal processing approaches that enhance the experience of listening over headphones, including how to implement a psychoacoustically reasonable equalizer for headphones, especially for noisy environments, and how to further
immerse headphone usage to be a part of our everyday lives. Thus, the
main objectives of this dissertation are to
1. provide means to demonstrate the sound quality of different headphones
especially in noisy environments,
2. enhance the mobile headphone listening experience using digital signal
processing,
3. improve the precision of graphic equalizers, and
4. develop novel digital methods for hear-through systems.
Objective 1 provides essential results that helps us to understand and
experience how different types of headphones behave in a noisy environment. After understanding the principles of objective 1, it possible to devise novel signal processing methods, as deﬁned in objective 2, which will
enhance the headphone listening experience, e.g., by adaptively equalizing the music according to ambient noise. In order to equalize the music
to mitigate the masking caused by ambient noise, it is necessary to satisfy
14
Introduction
objective 3 and to design an equalizer with frequency selectivity similar
to that of a human ear. Finally, to include headphones more into our everyday lives, the headphones should have a hear-through function that
allows us to wear the headphones during conversations and to use them
with augmented reality audio applications.
The contents of this dissertation consist of this overview part accompanied by seven peer-reviewed publications, from which three have been
published in international journals and four in international conferences.
These publications tackle the research questions aiming to satisfy the objectives. Publications I, II, and III tackle the ﬁrst objective by exploring headphone listening in noisy environments. Publication I introduces
a test environment for virtual headphone listening tests, which also enables the evaluation of headphones in the presence of virtual ambient
noise, whereas Publication II presents a perceptual frequency response
simulator, which simulates the effect of the auditory masking caused by
the ambient noise.
Objective 2 is addressed especially in Publication III, which introduces
an intelligent adaptive equalizer that is based on psychoacoustic models.
However, the groundwork for Publication III is done in Publications I, II,
and VI. Objective 3 is covered in Publications VI and VII, which present
an optimizing algorithm for a high-order graphic equalizer and a novel
high-precision graphic equalizer implemented with a parallel ﬁlter structure, respectively. The ﬁnal objective is addressed in Publications IV and
V, where digital hear-through systems are developed and evaluated.
This overview part of this dissertation is organized as follows. Section 2
discusses headset acoustics and their measurement techniques, including
ear canal simulators as well as magnitude frequency response and ambient noise isolation measurements. Section 3 introduces hear-through
systems along with the concept and applications of augmented reality audio. Section 4 examines the auditory masking phenomenon, including the
concepts of masking threshold and partial masking. Section 5 introduces
digital equalizers, giving also three examples of digital equalizer ﬁlters
used during this work. Section 6 summarizes the publications and their
main results, and Section 7 concludes this overview part.
15
Introduction
16
2. Headset Acoustics and
Measurements
This section introduces basic headset acoustics and their measurement
techniques. The focus is on in-ear headphones, since they are increasingly used in mobile music listening and in hands-free devices. Furthermore, in-ear headphones provide the tightest ﬁt of all headphones, which
leads to excellent ambient noise isolation, but at the same time introduces
problems, such as unnatural ear canal resonances and the occlusion effect,
due to the tightly blocked ear canal. The emphasis on the measurement
techniques is on the frequency response and ambient noise isolation measurements, since they greatly affect the perceived sound quality in noisy
environments.
Section 2.1 provides a brief introduction to different headphone types
and Section 2.2 introduces the basics of headphone acoustics, providing
background information for Publications I through V. Section 2.3 describes the history and current status of ear canal simulators that are
used in headphone measurements, related to Publications I–V. Sections
2.4 and 2.6 recapitulate frequency response and ambient noise isolation
measurement procedures. Section 2.7 discusses the occlusion effect, which
can easily degrade the user experience of an in-ear headset (Publications
IV and V).
2.1
Headphone Types
There are many different types of headphones, which all have their pros
and cons in divergent listening environments. Furthermore, different applications set distinct requirements for headphones, such as size, ambient
noise isolation, sound quality, and even cosmetic properties. ITU Telecommunication Standardization Sector (ITU-T) categorizes headphones into
four groups: circum-aural, supra-aural, intra-concha, and in-ear/insert
17
Headset Acoustics and Measurements
(a)
(b)
(c)
(d)
Figure 2.1. Categorization of headphones according to ITU-T Recommendation P.57,
where (a) shows a circum-aural, (b) a supra-aural, (c) an intra-concha, and
(d) an in-ear/insert headphone. Adapted from [4].
headphones [4]. Figure 2.1 shows the ITU-T categorization of the headphones, where (a) shows the circum-aural headphones, which are placed
completely around the ear against the head; (b) shows the supra-aural
headphones, which are placed on top of the pinna (also called on-the-ear
headphones); (c) shows the intra-concha headphones (button-type), which
are put loosely in the concha cavity; and (d) shows the in-ear headphones,
which are inserted tightly into the ear canal (similarly to earplugs).
When one or more microphones are wired to the headphones, they become a headset. Commonly, a mono microphone is installed in the wire
of stereo headphones. Often, the casing of the in-wire microphone is also
used as a remote control for the music player. When two microphones are
mounted into headphones in a way that they are placed near the ear canal
entrances of the user, the binaural cues of the user can be preserved well
[5].
2.2
Headset Acoustics
Normally, when a person is listening to surrounding sounds with open
ears, the incident sound waves are modiﬁed by the environment as well
as by the listener’s torso, head, and outer ear. However, when the same
surrounding sounds are captured with a conventional microphone and
later reproduced using headphones, all these natural modiﬁcations are
lost. Thus, headphone listening is quite different from the natural openear hearing that we are used to. Headphones also produce almost perfect
channel separation between the left and right channels, which results in
a situation where sound is perceived to come from inside the head.
18
Headset Acoustics and Measurements
Headphones can alter the acoustics of the open ear canal by blocking
it. This happens especially when using in-ear headphones, which tightly
block the entrance of the ear canal. An open ear canal is basically a tube,
which is open at one end and closed at the other by the ear drum. Thus,
the open ear canal acts as a quarter-wavelength resonator, which has it
ﬁrst resonance at the frequency
fr,open =
c
,
4leﬀ
(2.1)
where c is the speed of sound and leﬀ is the effective length of the ear
canal. The effective length of the ear canal is slightly longer than the
physical length due to the attached-mass effect [6]. The effective length
of the ear canal (as an open-ended tube) can be estimated as
leﬀ = l + 8r/3π,
(2.2)
where r is the average radius and l is the physical length of the ear canal
[6, 7].
An average ear canal has a length of approximately 27 mm and a diameter of about 7 mm [8]. When these measures are used in (2.1) and (2.2), the
effective length leﬀ is 30 mm and the ﬁrst quarter-wavelength resonance
occurs at the frequency of 2890 Hz, when the speed of sound is 346 m/s
(corresponding to 25◦ C). Typically, the ﬁrst resonance of the human ear
is located around 3 kHz with the maximum boost of approximately 20 dB
[9].
Figure 2.2 shows the effect of an open ear canal, where the contributions of the pinna, head, and upper torso are also included. The curve
represents the difference between the magnitude response of a Genelec
8030A active monitor measured ﬁrst 2 meters away in free-ﬁeld, and then
measured again from inside a human ear canal located 2 meters away.
The microphone was placed 1 cm deep into the ear canal for the second
measurement. As can be seen in Figure 2.2, the ﬁrst quarter-wavelength
resonance of this individual appears at 3.0 kHz.
When the ear canal is blocked by an in-ear headphone, the ear canal
acts as a half-wavelength resonator. The ﬁrst half-wavelength resonance
occurs at
fr,closed =
c
.
2l
(2.3)
The insertion of the in-ear headphone also shortens the length of the ear
canal (by approximately 5 mm) and increases the temperature of the air in
the ear to be about 35◦ C [10]. The speed of sound at 35◦ C is approximately
19
Magnitude (dB)
Headset Acoustics and Measurements
20
10
0
−10
100
1k
Frequency (Hz)
10k
Figure 2.2. Effect of the torso, outer ear, and ear canal on the magnitude of the frequency
response measured approximately 1 cm deep from the ear canal of a human
subject. The quarter-wavelength resonance, created by the open ear canal,
appears at 3.0 kHz.
352 m/s. Using these measures in Equation (2.3), the ﬁrst half-wavelength
resonance of a closed ear canal occurs at 7180 Hz. Thus, the insertion of
the headphone creates a new unnatural resonance as well as cancels the
natural resonance people are used to hearing.
2.3
Ear Canal Simulators
Headphones are typically measured using an ear canal simulator or a
dummy head. The idea of both of the measurement devices is to mimic
the actual average anatomy of the human ear canal, i.e., the measurement result should contain the ear canal resonances and the attenuation
at low frequencies due to the eardrum impedance [11, 10]. The measurement device should also provide a natural ﬁtting of the headphone to the
measurement device.
The sound pressure level (SPL), and thus also the magnitude response,
created by a headphone in a cavity depends directly on the impedance
of the cavity. The impedance of the cavity depends on the volume of the
cavity and on the characteristics of anything connected to it [11].
In the case of an in-ear headphone inserted into an ear canal, there
is a volume between the headphone tip and the eardrum (about 0.5 cm3
[11], and it depends on the in-ear headphone and the insertion depth
of the headphone) as well as an acoustic compliance due to the middle
ear cavity and the eardrum, which corresponds to a second volume (approximately 0.8 cm3 [11]). For low frequencies, the combined volume of
1.3 cm3 determines the impedance, and thus the SPL as well. When the
20
Headset Acoustics and Measurements
frequency increases, the impedance changes from stiffness-controlled to
mass-controlled as the mass of the eardrum becomes more signiﬁcant
(around 1.3 kHz) [10]. After that, the impedance corresponds only to that
of the volume between the headphone tip and the eardrum (in this example 0.5 cm3 ), since the mass of the eardrum blocks the impedance of the
middle ear [11, 10]. At higher frequencies, above around 7 kHz, the ear
canal resonances affect the impedance the most [10].
One of the earliest endeavours to build a dummy head, which included a
detailed structure of the pinnae as well as a correct representation of the
ear canal up to the eardrum location, was conducted by Alvar Wilska in
1938 [12]. Wilska was actually a physician, and he had access to a morgue,
where he was able to make a mold of the head of a male corpse. He then
made a gypsum replica of the head with the ear regions formed from gelatine. He also built his own microphones (with a membrane diameter of
13 mm) and even installed the microphones at the eardrum location at an
angle, which corresponds to the human anatomy [12].
The ﬁrst headphone measurement devices were couplers that had a
cylindrical volume and a calibrated microphone [11, 10], which simulated
the acoustical load of an average adult ear. The couplers were mainly
used to measure hearing aids and audiometry headphones. However, the
simple cylindrical volume did not produce satisfactory results, which led
to the development of more ear-like couplers [11, 13].
The pioneer of the modern ear simulator is Joseph Zwislocki, who designed an occluded ear canal simulator, which has a cylindrical cavity
corresponding to the volume of the ear canal, a microphone positioned
at the ear drum location called the drum reference point (DRP) [4], and
a mechanical acoustic network simulating the acoustic impedance of the
ear drum [14].
The acoustic network consists of several small cavities (typically from
two to ﬁve), which are all connected to the main cylindrical cavity via a
thin tube [11, 10]. The purpose of these thin tubes and cavities is to simulate the effective total volume of the ear, which changes with frequency, as
explained above. As the frequency rises, the impedance of the thin tubes
that connect the small cavities to the main cavity increases, which causes
them to close off, and thus the effective volume of the simulator gradually
falls from 1.3 cm3 to 0.6 cm3 , similarly as in the real ear [11].
Sachs and Burkhard [15, 16], Brüel [10], Voss and Allen [17], and Saltykov and Gebert [18] have evaluated the performance of the Zwislocki ear
21
Headset Acoustics and Measurements
canal simulator and the previous 2 cm3 headphone couplers (often called
2cc couplers). Their results showed that even though there is a signiﬁcant variability in the magnitude of the impedance for different human
ear canals, the Zwislocki ear simulators produce reasonably accurate results when compared to real ear measurements, whereas the 2 cm3 calibration couplers give worse results. Futhermore, Sachs and Burkhard
[15] suggested that better results could be achieved for the 2cc couplers,
if an electronic correction ﬁltering is applied.
Today, the IEC 60318-4 Ed 1.0b:2010 [19] and ANSI S3.25–2009 [20]
standards deﬁne the occluded ear simulators that are used to measure
headphones. The occluded ear simulator can be used to measure in-ear
headphones and hearing aids as such, and it can be extended to simulate
the complete ear canal and the outer ear [19, 21, 22]. IEC 60318-4 (2010)
deﬁnes the frequency range of the occluded ear canal simulator to be from
100 Hz to 10 kHz, whereas the previous standard IEC 60711, published in
1981, deﬁned an upper frequency limit of 8 kHz. Furthermore, below 100
Hz and above 10 kHz, the simulator can be used as an acoustic coupler
at frequencies down to 20 Hz and up to 16 kHz. However, the occluded
ear simulator does not simulate the human ear accurately in the extended
frequency range.
2.4
Frequency Response Measurements
The frequency response measurement is a good way to study the performance of headphones. The magnitude frequency response shows the
headphones’ ability to reproduce different audible frequencies, which determines the perceived timbral quality of the headphones. A widely used
technique to measure frequency responses is a swept-sine technique. It
has been proposed by Berkhout et al. [23], Griesinger [24, 25], Farina
[26, 27], as well as Müller and Massarani [28]. The swept-sine technique
as well as other measurement techniques are compared by Stan et al. [29].
The basic idea in sweep measurements is to apply a known signal x(t),
which has a sinusoidal shape with exponentially increasing frequency,
through the headphones, which are ﬁtted in an ear canal simulator or
a dummy head, and record the response of the system y(t) at the DRP.
The impulse response of the system h(t) is calculated from the x(t) and
22
Headset Acoustics and Measurements
−20
Magnitude (dB)
−30
−40
−50
Circum−aural
Supra−aural
Intra−concha
In−ear
−60
−70
−80
100
1k
Frequency (Hz)
10k
Figure 2.3. Magnitude frequency responses of different headphones.
y(t) with the fast Fourier transform (FFT) deconvolution
h(t) = IFFT
FFT(y(t))
,
FFT(x(t))
(2.4)
where IFFT is the inverse fast Fourier transform.
Figure 2.3 shows four magnitude frequency responses of different headphones measured using a dummy head and the sine-sweep technique. The
responses are normalized to have the same magnitude value at 1 kHz. As
can be seen in the ﬁgure, the in-ear headphone has the strongest low frequency response.
This is caused by the pressure chamber principle [30, 31], which affects
low and middle frequencies, when sound is produced in small, airtight
enclosures. The force of the headphone driver pumps the air inside the
ear canal cavity producing a sound pressure proportional to the driver
excursion. Moreover, when the wavelength is large compared to the dimensions of the ear canal (up to about 2 kHz [30]), the sound pressure is
distributed uniformly in the volume, which enables the production of high
SPLs at low frequencies. Thus, the pressure inside the closed ear canal is
in phase with the volume displacement of the transducer membrane and
the amplitude is proportional to that volume displacement [30].
The sine-sweep measurement technique was an essential tool in Publication I, but it was also utilized in Publications II–V and VII. Publication
I introduced a signal processing framework for virtual headphone listening tests in simulated noisy environments. Briolle and Voinier proposed
the idea of simulating headphones by using digital ﬁlters in 1992 [32].
Hirvonen et al. [33] presented a virtual headphone listening test methodology, which was implemented using dummy-head recordings. Hiekkanen
23
Headset Acoustics and Measurements
et al. [34] have proposed a method to virtually evaluate and compare loudspeakers using calibrated headphones. Furthermore, a recent publication
by Olive et al. [35] tackles the virtual headphone listening task by using inﬁnite impulse response (IIR) ﬁlters constructed based on headphone
measurements to simulate the headphones.
Similarly as in [35], the main idea in Publication I is to simulate different headphones with digital ﬁlters, which allows the use of arbitrary music, speech, or noise as the source material. Thus, a wide range of impulse
responses of different headphones were measured using the sine-sweep
technique and a standardized head-and-torso simulator. The measured
impulse responses were used as ﬁnite impulse response (FIR) ﬁlters to
simulate the measured headphones. Furthermore, the ambient noise isolation capabilities of the headphones were also measured and simulated
using FIR ﬁlters (see Section 2.6).
2.5
Headphone Equalization
In the simulator of Publication I, all the modeled headphones are listened
to through one pair of calibrated headphones. The pair of headphones
used with the simulator must be compensated so that they introduce as
little timbre coloration to the system as possible. This is done by ﬂattening
or whitening the headphone response.
A typical method to implement the compensation of the headphones
is to use an inverse IIR ﬁlter constructed from the magnitude response
measurement of the headphone [33, 35]. Olive et al. [35] used a slightly
smoothed minimum-phase IIR correction ﬁlter, which did not apply ﬁltering above 10 kHz. Lorho [36] ﬁtted a smooth curve to the headphone’s
magnitude response to approximate the mean response of his measurements. The ﬁtted curve had a ﬂat low-frequency shape, the most accurate match in the range of 2–4 kHz (around where the ﬁrst quarterwavelength resonance occurs), and a looser ﬁt at high frequencies. Finally, he designed a linear-phase inverse ﬁlter from the smoothed approximation curve.
There can be problems with the inverse ﬁlter when the measured magnitude response has deep notches, because they will convert into high
peaks in the inversion. This was the case in [34] and in Publication I of
this work. Hiekkanen et al. [34] ﬁrst smoothed the measured magnitude
response (with a moving 1/48-octave Hann window) and then inverted
24
Headset Acoustics and Measurements
the smoothed response. They then calculated a reference level based on
the average level of the inverted response (between 40–4000 Hz). After
that, they compressed all the peaks (above 4 kHz) exceeding the reference
level. Finally, they created a minimum-phase impulse response from the
inverted and peak deducted magnitude response.
In Publication I the measured magnitude response of the headphones
was ﬁrst modeled with linear prediction coefﬁcients (LPC). The LPC technique results in a minimum-phase all-pole ﬁlter, which can easily be inverted without any stability issues. A peak reduction (or actually a notch
reduction) technique was also used, and it was implemented by ’ﬁlling’
the notch in the measured magnitude response in the frequency domain,
after which the LPC technique was applied to the corrected magnitude
response where the notch was compensated.
2.6
Ambient Noise Isolation
The isolation capabilities of headphones are determined by the physical
structure of the headphones as well as by the coupling of the headphones
to the user’s ears. Circum-aural and supra-aural headphones are typically
coupled tightly against the head and the pinna, respectively. Furthermore, these headphones can have open or closed backs, where the open
back refers to earphone shells with leakage routes intentionally built into
them. Intra-concha headphones usually sit loosely in the concha cavity,
thus providing poor ambient noise isolation for the user. However, in-ear
headphones are used similarly as earplugs, and they are designed to have
good isolation properties.
Figure 2.4 shows the measured isolation curves of four different types of
headphones. In Figure 2.4, zero dB means that there is no attenuation at
all and negative values indicate increasing attenuation. The black curve
illustrates the isolation of open-back circum-aural headphones, the green
curve represents the isolation of closed-back supra-aural headphones, the
red curve shows the isolation of intra-concha headphones, and ﬁnally the
purple curve shows the isolation of in-ear headphones. As can be seen,
the in-ear headphones have clearly the best ambient noise isolation capability of these headphones, while the open-back circum-aural and the
intra-concha headphones have the worst sound isolation, as can be expected.
The isolation properties of headphones are further explored in Publica-
25
Headset Acoustics and Measurements
10
Attenuation (dB)
0
−10
−20
−30
−40
−50
Circum−aural
Supra−aural
Intra−concha
In−ear
100
1k
Frequency (Hz)
10k
Figure 2.4. Measured isolation curves of different headphone types.
tions I–V, where the ﬁrst three publications estimate and simulate the
amount of ambient noise in the ear canal in a typical music listening and
communication applications and the latter two consider how the leakage
of ambient noise affects the hear-through applications (see Section 3).
The isolation capabilities of headphones can be improved with active
noise control (ANC) [37, 38, 39, 40, 41]. The idea in ANC is to actively
reduce the ambient noise at the ear canal by creating an anti-noise signal,
which has the same sound pressure in magnitude but is opposite in phase
to the noise signal. The utilization of the ANC in headsets dates back
to the 1950s, where Simshauser and Hawley [42] conducted pure-tone
experiments with an ANC headset.
The two basic forms of ANC are feedforward [40] and feedback systems [41]. The main difference in these systems is the position of the
microphones that are used to capture ambient noise. Adaptive feedforward ANC uses an external reference microphone outside the headphone
to capture the noise and a microphone inside the headphone to monitor
the error signal. Feedback ANC has only the internal error microphone
that is used to capture the noise and to monitor the error signal. The
feedforward and feedback structures can also be combined to create more
efﬁcient noise attenuation [39, 43].
Figure 2.5 shows the effect of a feedback ANC, measured from a commercial closed-back circum-aural ANC headset. The feedback ANC works
best at low frequencies, while it has a negligible effect at high frequencies.
Furthermore, the feedback ANC typically ampliﬁes the noise at its transition band, around 1 kHz [39], as shown in Figure 2.5. The transition
band is the frequency band between the low frequencies, where the ANC
26
Headset Acoustics and Measurements
10
ANC off
ANC on
Attenuation (dB)
0
−10
−20
−30
−40
−50
100
1k
Frequency (Hz)
10k
Figure 2.5. Effect of active noise control.
operates efﬁciently and the high frequencies, where the effect of the ANC
is negligible.
2.7
Occlusion Effect
The occlusion effect refers to a situation where one’s ears are blocked by a
headset or a hearing aid and their own voice sounds loud and hollow. This
effect is due to the bone conducted sounds that are conveyed to the ear
canals [44]. The occlusion effect is experienced as annoying and unnatural, since the natural perception of one’s own voice is essential to normal
communication [45]. With music listening this is rarely a problem, since
the user rarely talks while listening to music. However, with ARA applications and hearing aids, the occlusion effect can be a real nuisance.
Normally, when the ear canal is not blocked, the bone-conducted sounds
can escape through the ear canal opening into the environment outside
the ear and no extra sound pressure is generated in the ear canal [30, 46],
as illustrated in Figure 2.6(a). This is the normal situation that people
are used to hear. However, when the entrance of the ear canal is blocked,
the perceived sound of one’s own voice is changed [45]. This is because
the bone-conducted sounds that are now shut in the ear canal obey the
pressure chamber principle, as discussed in Section 2.4. This ampliﬁes
the perception of the bone-conducted sounds when compared to the open
ear case [30, 11, 47, 48, 46], as illustrated in Figure 2.6(b). In addition
to the bone-conducted sounds, the air-conducted sounds are attenuated
when the the ear canal is occluded, which further accentuates the distorted perception of one’s own voice [45].
27
Headset Acoustics and Measurements
Bone
conducted
sound
Bone
conducted
sound
p
p
(a)
(b)
Figure 2.6. Occlusion effect. Diagram (a) illustrates the open ear, where the bone conducted sound radiates through the ear canal opening and (b) illustrates the
occluded ear, where the bone conducted sound is ampliﬁed inside the ear
canal. Adapted from [30].
The occlusion effect can be passively reduced by introducing vents in
the headset casing. However, the vents decrease the ambient noise isolation of the headset, which is usually undesirable. Furthermore, the vents
increase the risk of acoustic feedback when the headset microphone is
placed near the earpiece. The occlusion effect can also be reduced actively,
which typically uses the active feedback cancellation method similarly as
a feedback ANC. That is, there is an internal microphone inside the ear
canal connected to the signal chain with a feedback loop [49]. According
to Meija et al. [49], active occlusion reduction can provide 15 dB of occlusion reduction around 300 Hz, which results in a more natural perception
of one’s own voice and therefore increases the comfort of using a headset.
Furthermore, active occlusion reduction does not deteriorate the ambient
noise isolation capability of a headset, since no additional vents or leakage
paths are needed.
28
3. Augmented Reality Audio and
Hear-Through Systems
This section presents the concept of ARA as well as touches on the subject
of active hearing protection. According to the World Health Organization
[50], more than 360 million people have disabling hearing loss. The number is so large that the current production is less than 10 % of the global
need.
It is extremely important to protect one’s hearing, and not only from loud
environmental noises, but also from loud music listening, which can cause
hearing loss as well. Furthermore, it is widely accepted that temporary
hearing impairments can lead to accumulated cellular damage, which can
cause permanent hearing loss [51]. This section provides background information for Publications III, IV and V.
3.1
Active Hearing Protection
Noise-induced hearing loss (NIHL) occurs when we are exposed to very
loud sounds (over 100 dB) or sounds that are loud (over 85 dB) for a long
period of time. NIHL can be roughly divided into two categories, namely
work-related and self-imposed NIHLs. The ﬁrst one can be controlled with
regulations, which, e.g., enforce employees to wear hearing protection in
loud working environments. However, laws or regulations cannot prevent
self-imposed NIHL, such as that caused by listening to loud music or live
concerts.
People’s listening habits when using headphones have been studied, e.g.,
in [52, 53], where the highest user-set music levels reached over 100 dB
in the presence of background noise [52]. Studies conducted during live
music concerts [54, 55] indicate that the A-weighted exposure levels can
exceed 100 dB during a concert. Furthermore, Clark et al. [54] discovered that ﬁve of the six subjects had signiﬁcant hearing threshold shifts
29
Augmented Reality Audio and Hear-Through Systems
(< 50 dB) mainly around the 4-kHz region after a rock concert. The hearing of all subjects returned to normal 16 hours after the concert.
A major problem with passive hearing protection is that it impairs speech
intelligibility as well as blocks informative sounds, such as alarms or instructions. With active hearing protectors, it is possible to implement
a hear-through function, which lets low-level ambient sounds, such as
speech, through the hearing protector by capturing the sounds with an
included microphone and by reproducing them with a loudspeaker that
is implemented in the hearing protector. When the level of the ambient
sounds increases, the gain of the reproduced ambient sounds is decreased,
and eventually when the gain goes to zero, the harmfully loud sounds are
passively attenuated by the hearing protector [56].
Publications III and V tackle the NIHL problem by offering signal processing applications which reduce the noise exposure caused by music.
Publication III introduces an intelligent, perceptually motivated equalizer for mobile headphone listening that adapts to different noisy environments. The idea is to boost only those frequency regions that actually
need ampliﬁcation, and in this way minimize the volume increase caused
by the boosting. Publication V presents a hear-through application that
can be used during a loud concert to limit the noise exposure caused by the
live music. The hear-through function is user controllable, which allows
the user to do some mixing of their own during the concert.
Furthermore, the idea to enhance the target signal in the presence of
noise is also used in mobile communication applications [57, 58, 59] as
well as in automotive audio [60, 61]. In mobile communication applications, the target signal is typically speech, and this technique is called
near-end listening enhancement.
3.2
Augmented Reality Audio
Augmented reality is deﬁned as a real-time combination of real and virtual worlds [3]. In ARA, a person’s natural sound environment is extended
with virtual sounds by mixing the two together so that they are heard simultaneously.
The mobile usage of ARA requires a headset with binaural microphones
[62, 63, 64]. A prerequisite of an ARA headset is that it should be able to
relay the natural sound environment captured by the microphones to the
ear canal unaltered, and with minimal latency. This copy of the natural
30
Augmented Reality Audio and Hear-Through Systems
Virtual
sounds
Binaural
signals to
distant user
Figure 3.1. Block diagram of the ARA system.
sound environment that has propagated through the ARA system is called
a pseudoacoustic environment [3].
However, the acoustics of the ARA headset differs from normal open ear
acoustics, and hence, the ARA headset requires equalization in order to
sound natural. The equalization is realized with an ARA mixer [63]. The
sound quality of the pseudoacoustic environment must be good so that
the users can continuously wear the ARA headset without fatigue. The
usability issues of the ARA headset have been studied by Tikander [65].
Because of the low latency requirements, earlier ARA systems were
implemented using analog components [63] in order to avoid the combﬁltering effect (see Section 3.3) caused by the delayed pseudoacoustic sound.
Publication IV introduces a novel digital version of the ARA system, as
well as analyses how the delay caused by the digital signal processing
affects the perceived signal.
3.2.1
ARA Applications
Figure 3.1 shows the system diagram for ARA applications. Virtual sounds
can be processed, e.g., with head-related transfer functions (HRTF) to
place the virtual sounds into the three dimensional space around the user
[66, 67]. The ARA mixer and equalizer is used for the equalization of the
pseudoacoustic representation as well as for routing and mixing of all the
signals involved in the system. Furthermore, the binaural microphone
signals can be sent to distant users for communication purposes.
The ARA technology enables the implementation of different innovative applications, including applications in communication and information services, such as teleconferencing and binaural telephony with good
telepresence [68, 69], since the remote user can hear the same pseudoacoustic environment as the near-end user does.
31
Augmented Reality Audio and Hear-Through Systems
There are also many possible application scenarios when the position
and orientation of the user is known, including an eyes-free navigation
system [70, 71], the virtual tourist guide [65], and location-based games
[72]. The positioning of the user can be done using the Global Positioning System (GPS) whereas determining of the user’s orientation requires
head tracking [73].
The ARA system can also be used to increase situational awareness [56].
Furthermore, it is possible to estimate different acoustic parameters from
the surrounding environment, such as room reﬂections [74] and reverberation time [75].
The ARA system could also be used as an assistive listening device.
In Publication V, the hear-through headset is used to attenuate ambient sounds while the pseudoacoustic representation is made adjustable
for the user. Enhancing ambient sounds would also be possible with current technology. The digital ARA technology presented in Publication IV
would enable an adjustable platform for a user-controllable ARA system
that could be individually tuned to the user’s needs.
3.3
Comb-Filtering Effect
In ARA, the pseudoacoustic representation undergoes some amount of delay due to the equalization ﬁlters. However, the largest delays are typically caused by the analog-to-digital and digital-to-analog converters operating before and after the actual equalization, respectively. The delay
of a laptop and an external FireWire audio interface measured in Publication IV, including both the delays of the analog-to-digital and the digitalto-analog converter, was 8.6 and 21 ms, respectively. Furthermore, the
delay introduced by the converters of the DSP evaluation board used in
Publication IV was approximately 0.95 ms.
In addition to the pseudoacoustic sound, the ambient sounds leak through
and around the ARA headset. The leaked sound and the delayed pseudoacoustic sound are summed at the ear drum of the user, and because of the
delay, it can easily result in a comb-ﬁltering effect [76, 77]. The combﬁltering effect occurring in a hear-through device can be simulated with
an FIR comb ﬁlter:
H(z) = gd + geq z −L ,
(3.1)
where gd is the gain for the direct leaked sound, geq is the gain for the
equalized pseudoacoustic sound, and L is the delay in samples.
32
Augmented Reality Audio and Hear-Through Systems
Magnitude (dB)
10
0
−10
−20
−30
−40
100
1k
Frequency (Hz)
10k
(a)
Magnitude (dB)
10
0
−10
−20
−30
−40
100
1k
Frequency (Hz)
10k
(b)
Figure 3.2. Magnitude frequency response of the FIR comb ﬁlter deﬁned in Equation
(3.1), when the delay is 2 ms. In (a) the values of gd and geq = 0.5 and in (b)
gd = 0.1 and geq = 0.9.
The worst-case scenario occurs when both of the signals have the same
amount of energy gd = geq . Figure 3.2(a) shows the comb-ﬁltering effect
when the gains are set to gd = geq = 0.5 and the delay is 2 ms. The value
of 0.5 for the gains was chosen because when the sum of the gains is one
the top of the peaks lie at 0 dB.
Figure 3.2(b) shows the magnitude response of an FIR comb ﬁlter described by Equation (3.1), when the delay between the signals is 2 ms, as
for the ﬁlter used to obtain Figure 3.2(a). However, now the direct sound
is attenuated 20 dB, and thus the values of gd and geq are 0.1 and 0.9,
respectively. As can be seen, the dips are at the same frequencies as in
Figure 3.2(a), but their depth is reduced dramatically. In fact, with the
20-dB attenuation, the depth of the dips is only 2 dB.
The audibility of the comb-ﬁltering effect has been previously studied,
e.g., in [78, 79, 76]. The amount of attenuation needed between the direct and delayed sound for the comb-ﬁltering coloration to be inaudible
33
Augmented Reality Audio and Hear-Through Systems
depends on the delay and the type of audio that is listened to. The result of Schuller and Härmä [79] showed that the level difference between
the direct and delayed sound should be around 26–30 dB in order to be
inaudible. The result was averaged over different audio test material.
Brunner et al. [76] conducted a listening test in order to determine the audibility of the comb-ﬁltering effect for speech, piano, and snare drum test
signals. Their results indicated that when the delay is 0.5–3 ms, which
corresponds to a typical latency of an ARA headset system, the average
level difference needed in order for the comb-ﬁltering effect to be inaudible was approximately 12 dB for the speech and piano, and 17 dB for the
snare drum sample. The highest required attenuations (based on single
subjects) for the piano, speech, and snare drum samples, were 22, 23, and
27 dB, respectively.
This kind of isolation cannot be accomplished with all headphones, but
with in-ear headphones it is possible, since they can provide more than
20-dB attenuation above 1 kHz, as shown in Figure 2.4. Furthermore, the
idea in the ARA system is to wear the headset continuously and not to
compare the pseudoacoustic representation of the sound directly with the
real-world sounds by removing the headset. This allows the user of the
headset to get accustomed to minor timbral distortions.
34
4. Auditory Masking
Psychoacoustics is aiming to understand and characterize human auditory perception, especially the time-frequency processing of the inner-ear
[80]. Auditory masking is a common psychoacoustic phenomenon that occurs all the time in our hearing system. Auditory masking refers to a
situation where one sound (a masker) affects the perceived loudness of
another sound (a maskee). The maskee can either be completely masked
to be inaudible by the masker or just reduced in loudness. The latter
instance is referred to as partial masking.
Auditory masking has been tackled for quite a long time. Fletcher and
Munson [81] have studied the relation between the loudness of complex
sounds and masking already back in 1937. Despite the long history of
research in the area, the auditory system, including auditory masking
and hearing models, is still an active research topic [82, 83].
Knowledge of auditory masking has also been utilized in audio coding
for 35 years, since Schroeder et al. [84] studied the optimization of speech
codecs by utilizing the knowledge of human auditory masking. The idea
in audio coding is to identify irrelevant information by analyzing the audio signal and then reducing this information from the signal according
to psychoacoustical principles, including the analysis of absolute threshold of hearing, the masking threshold, and the spreading of the masking
across the critical bands [85, 86, 87].
This Section provides the psychoacoustic context for Publications II and
III.
4.1
Critical Bands
The critical band is an important concept when describing the time-frequency resolution of the human auditory system. Basically, it describes
35
Auditory Masking
30
100
1k
300
Frequency (Hz)
10k
3k
Figure 4.1. Bark band edges on a logarithmic scale.
how the basilar membrane interprets different frequencies. The mechanical properties of the basilar membrane change as a function of its position.
At the beginning, the membrane is quite narrow and stiff, while at the end
it is wider and less stiff [88]. Every position on the basilar membrane reacts differently to different frequencies. In fact, the basilar membrane
conducts a frequency-to-position transformation of sound [89, 90, 91].
The critical bandwidth is based on the frequency-speciﬁc positions of
the basilar membrane. The hearing system analyzes wide-band sound in
a way that the sounds within one critical band are analyzed as a whole.
In terms of auditory masking, this means that the level of masking within
a critical band is constant [92].
The Bark scale was proposed by Zwicker in 1961 [93] to represent the
critical bands such that one Bark corresponds to one critical band. The
critical bands behave linearly up to 500 Hz, and after that the behavior is
mainly logarithmic. Figure 4.1 plots the band edges of the Bark scale, as
introduced in [93]. Furthermore, the conversion from frequency in Hertz
to the Bark scale can be approximately calculated from
ν = 13 arctan
0.76f
kHz
+ 3.5 arctan
f
7.5kHz
2
,
(4.1)
where f is the frequency in Hertz and ν is the mapped frequency in Bark
units [94].
4.2
Estimation of Masking Threshold
In order to estimate the inaudible critical bands of the maskee in the presence of a masker, a masking threshold is calculated. According to Zwicker
and Fastl [94], the masking threshold refers to the SPL needed for a maskee to be just audible in the presence of a masker. The calculation of
masking threshold used in this work (Publications II and III) is based on
an audio signal coding technique presented by Johnston [85]. There are
36
Auditory Masking
Masking energy offset (U)
Sp
re
ad
i
ng
fu
nc
tio
SPL (dB)
n
(B
)
SPL of masker (LM)
Critical band (Bark)
Figure 4.2. Masking threshold estimation.
more recent and advanced auditory models available as well, e.g., [95, 96].
Nevertheless, the technique by Johnston was chosen, since the applications in Publications II and III required only an estimate of the masking
threshold and not more complex auditory models or attributes. The Johnston method provides a straightforward method to calculate the masking
threshold, and it is sufﬁciently low in complexity to be implemented in
real-time.
The masking threshold is calculated in steps [85], including the following: calculating the energy of the masker signal, mapping the frequency
scale onto the Bark scale and calculating the average energy for each critical band, applying a spreading function to each Bark band, summing the
individual spread masking curves into one overall spread masking threshold, estimating the tonality of the masker and calculating the offset for the
overall spread masking threshold, and ﬁnally, accounting for the absolute
threshold of hearing. All the equations needed to calculate the masking
threshold are found in Publications II and III.
Figure 4.2 illustrates the masking threshold estimation of one Bark
band, where LM is the energy of the Bark band, B is the spreading function, and U is the masking-energy offset. The masking-energy offset is
determined based on the tonality of the masker that can be estimated
using the spectral ﬂatness measure, which is deﬁned as the ratio of the
geometric mean to the arithmetic mean of the power spectrum [85]. A
ratio close to one indicates that the spectrum is ﬂat and the signal has
decorrelated noisy content, whereas a ratio close to zero implies that the
spectrum has narrowband tonal components [80]. The masking energy
offset is required since it is known that a noise-like masker masks more
effectively than a tonal masker [95].
37
Auditory Masking
SPL (dB)
Masker
A
B
Ma
ski
ng
thre
sho
ld
Critical band (Bark)
Figure 4.3. Partial masking effect, where two tones (unﬁlled markers) are in the presence of a masker.
4.3
Partial Masking
The maskee is not necessarily masked completely in the presence of the
masker. When the maskee is just above the masking threshold, it is audible but perceived to be reduced in loudness. In such a case, the maskee is partially masked. Thus, the masker does not only shift the absolute threshold of hearing to the masked threshold, but it also produces a
masked loudness curve [94]. The effect of partial masking is the strongest
near the masking threshold. When the level of the maskee increases, the
effect of the partial masking decreases at a slightly faster rate. As the
level of the maskee increases to a sufﬁciently high level, the effect of partial masking ends, and the maskee is perceived at the same level in the
presence of the masker as in quiet [97].
Figure 4.3 shows a situation where two tones with the same SPL are in
the presence of a masker, one of which is at slightly higher frequency (B)
than the masker and the other at much lower frequency (A). The thick
stem with the ﬁlled marker shows the SPL of the masker, whereas the
two stems with unﬁlled round markers (A and B) indicate the SPL of the
two tones. The triangle marker is the perceived SPL of the high-frequency
tone. As can be seen in Figure 4.3, the high-frequency tone (B) is above
the masking threshold but still close to it. Thus, the tone is audible but its
perceived SPL is reduced, as indicated by the triangle marker. However,
the low-frequency tone (A) is nowhere near the masking threshold, and
thus, it is not affected by the masker at all. The low-frequency tone is
perceived at the same level as it would be perceived without the masker.
38
5. Digital Equalizers
This section presents the basics of digital equalizers, which are widely
used in this work, as well as a parametric and a graphic equalizer design,
since they are used in Publications II-VII. Furthermore, the concept of
a parallel equalizer is introduced, since Publication VII is based on this
concept.
5.1
Equalizers
Equalizers are often used to correct the performance of audio systems. In
fact, equalization can be said to be one of the most common and important audio processing methods. The expression ‘equalize’ originates from
its early application, which was to ﬂatten out a frequency response. However, nowadays, the objective is not necessarily to ﬂatten out the frequency
response, but to alter it to meet desired requirements.
A certain set of ﬁlters is needed in the implementation of an equalizer.
These equalizer ﬁlters can be tuned to adjust the equalizer to suit the
given application. Typical equalizer ﬁlters are shelving ﬁlters and peak
or notch ﬁlters. Shelving ﬁlters are used to weight low or high frequencies. High-frequency and low-frequency shelving ﬁlters boost or attenuate frequencies above or below the selected cut-off frequency, respectively.
Shelving ﬁlters are usually found on the treble and bass controls of home
and car audio systems. Shelving ﬁlters are useful when adjusting the
overall tonal properties. However, more precise ﬁlters are often required.
A peak or notch ﬁlter can be targeted to a speciﬁc frequency band at any
desired frequency.
The ﬁrst time that an equalizer was used to improve the quality of reproduced sound was in the 1930s, when motion pictures with recorded sound
tracks emerged. Sound recording systems as well as reproduction systems
39
Digital Equalizers
Figure 5.1. Front panel of a graphic equalizer.
that were installed in theaters were poor and thus needed equalization
[98, 99]. The ﬁrst stand-alone, fully digital equalizer was introduced in
1987 by Yamaha, namely the DEQ7, which included features such as 30
preset equalizer programs and a parametric equalizer [99, 100].
The two most common types of equalizers are the parametric [101, 102,
103, 104, 105] and graphic [106, 107, 108, 109, 110] equalizers. With
a parametric equalizer, the user can control the center frequency, bandwidth, and gain of the equalizer ﬁlters, whereas with graphic equalizer
the only user-controllable parameter is the gain. The gains are controlled
using sliders that plot the approximate magnitude response of the equalizer, as shown in Figure 5.1.
Nowadays equalizers are widely used in audio signal processing, which
include a variety of applications, such as enhancing a loudspeaker response [111, 112, 113] as well as the interaction between a loudspeaker
and a room [114, 115, 116, 117, 118], equalization of headphones in order
to provide a natural music listening experience [36, 119, 120, 121, 122] or
a natural hear-through experience [123, 124, 125, 126], and enhancement
of recorded music [127, 128].
Furthermore, an equalizer can be considered to be a ﬁlter bank, since
it has an array of bandpass ﬁlters that are used to control different subbands of the input signal. However, more advanced ﬁlter banks, such as
quadrature mirror ﬁlter banks, are often implemented with the analysissynthesis system using multirate signal processing [129]. In the analysis
bank, the signal is split into different bands, and the sub-bands are decimated by an appropriate factor. Then, the sub-band signals can be processed independently, for example, by using different coding or compressing the sub-bands differently. After that, the signal is reconstructed by
interpolating the sub-band signals [129]. Although multirate ﬁlter banks
40
Digital Equalizers
offer a wider range of signal processing possibilities, the advantage of an
equalizer is that it does not require the splitting and reconstruction of the
audio signal.
5.2
Parametric Equalizer Design
Regalia and Mitra [101] introduced a digital parametric equalizer ﬁlter,
which is based on an allpass ﬁlter structure. The Regalia–Mitra ﬁlter
enables gain adjustments at speciﬁed frequencies, while it leaves the rest
of the spectrum unaffected. Furthermore, the equalizer parameters can
be individually tuned.
The transfer function of the ﬁlter can be expressed as
K
1
H(z) = [1 + A(z)] + [1 − A(z)],
2
2
(5.1)
where K speciﬁes the gain of the ﬁlter and A(z) is an allpass ﬁlter [101].
When A(z) is a ﬁrst-order allpass, the ﬁlter is a shelving ﬁlter. When A(z)
is a second-order allpass, the ﬁlter is a peak or notch ﬁlter. The secondorder allpass ﬁlter commonly used in (5.1) has the transfer function
A(z) =
a + b(1 + a)z −1 + z −2
,
1 + b(1 + a)z −1 + az −2
where
a=
1 − tan(Ω/2)
,
1 + tan(Ω/2)
b = − cos(ω0 ),
(5.2)
(5.3)
(5.4)
Ω denotes the normalized ﬁlter bandwidth, and ω0 the normalized center
frequency. When K > 1, the ﬁlter is a peak ﬁlter and when K < 1, it is
notch ﬁlter.
The peaks and notches are not symmetric in the original Regalia–Mitra
ﬁlter structure, i.e., the bandwidth of the notches is smaller than that of
the peak ﬁlters [101]. Figure 5.2(a) shows the asymmetry between the
peak and notch ﬁlters. However, Zölzer and Boltze [130] extended the
second-order Regalia-Mitra structure in order to have symmetrical peaks
and notches. The only change to the original structure concerns the frequency parameter a, which becomes
ac =
K − tan Ω/2
,
K + tan Ω/2
when K < 1.
(5.5)
Figure 5.2(b) shows the magnitude response of the peak and notch ﬁlters,
when ac is used.
41
Digital Equalizers
20
10
10
0
0
−10
−10
Gain (dB)
20
−20
100
1k
Frequency (Hz)
10k
−20
100
(a)
1k
Frequency (Hz)
10k
(b)
Figure 5.2. Effect of ac , where (a) is the response of the original Regalia-Mitra design
and (b) is that of the improved design, which uses the ac parameter for notch
ﬁlters.
A slightly different approach to achieve the symmetry between the peaks
and notches is presented by Fontana and Karjalainen [131]. Their implementation enables switching between peak and notch ﬁlter structures
such that both structures share a common allpass section.
5.3
High-Order Graphic Equalizer Design
Holters and Zölzer [108] presented a high-order graphic equalizer design
with minimum-phase behavior. The equalizer has almost independent
gain control at each band due to the high ﬁlter order. The design is based
on a high-order digital parametric equalizer [132], which was later extended by Holters and Zölzer [133]. The design utilizes recursive ﬁlters,
where fourth-order ﬁlter sections are cascaded in order to achieve higher
ﬁlter orders.
Figure 5.3 shows the recursive structure of the graphic equalizer, where
P is the number of fourth-order sections that are cascaded. The dashed
lines show the lower and upper limits of the ninth Bark band, whose center frequency is 1 kHz and bandwidth is 160 Hz. As can be seen, the ﬁlter
becomes more and more accurate as the number of fourth-order sections
is increased. A more detailed description of the ﬁlter design is given, e.g.,
in Publication VI.
Publications II and III use a Bark-band graphic equalizer implemented
using this high-order ﬁlter design [108]. The graphic equalizer in Publication II is used to simulate the perception of music in a noisy environment,
i.e., the graphic equalizer is used to suppress those Bark bands that are
either completely or partially masked by ambient noise. The masking information is obtained from psychoacoustical models, as described in Sec-
42
Digital Equalizers
H1(z)
H2(z)
HP(z)
Figure 5.3. High-order graphic equalizer, where the gain is set to -20 dB. The dashed line
shows the lower and upper limit of the ninth Bark band.
tion 4. Based on the estimate of the masking threshold and the partial
masking effect, the gains of the high-order graphic equalizer are adjusted
to suppress the music, so that if the energy of the music in a certain Bark
band is below the masking threshold it is attenuated 50 dB. When the
music is just partially masked, it is attenuated according to the partial
masking model.
The lessons learned from Publication II was then exploited in Publication III, where the design goal was to implement an adaptive perceptual
equalizer for headphones which estimates the energy of the music and
ambient noise inside the user’s ear canal based on the measured characteristics of the headphones.
Furthermore, Publication VI introduces a novel ﬁlter optimization algorithm for the high-order graphic equalizer. Especially in the Bark-band
implementation of the graphic equalizer, the errors in the magnitude response around the transition bands of the ﬁlters increase at high and low
frequencies (due to the bilinear transform at high frequencies [108] and
logarithmically non-uniform bandwidths at low frequencies). The errors
occur due to the non-optimal overlapping of the adjacent ﬁlter slopes.
The proposed optimization algorithm in Publication VI minimizes these
errors by iteratively optimizing the orders of adjacent ﬁlters. The optimization affects the shape of the ﬁlter slope in the transition band, which
makes the search for the optimum ﬁlter order possible by comparing it to
the slope of the adjacent ﬁlter.
Figure 5.4 shows the idea behind the optimization scheme, where in (a)
the black curve is a target band ﬁlter (center frequency 350 Hz, bandwidth
100 Hz, gain −20 dB, and the number of fourth-order sections P = 2) and
the colored curves are ﬁlter candidates of different order for the adjacent band (center frequency 250 Hz and bandwidth 100 Hz) with different
number of fourth-order sections P = 2–5. As can be seen in Figure 5.4(a),
43
Digital Equalizers
0
3
Error (dB)
−5
Magnitude (dB)
P=2
P=3
P=4
P=5
−10
P=2
P=2
P=3
P=4
P=5
−15
−20
200
300
Frequency (Hz)
400
(a)
2
1
0
240
260
280
300
320
Frequency (Hz)
340
360
(b)
Figure 5.4. Example of the interaction occurring around a transition band between two
adjacent band ﬁlters, where (a) shows the magnitude response of a target
band ﬁlter (black curve, P = 2) and a set of magnitude responses of ﬁlter
candidates on the adjacent lower band (colored curves, P = 2, 3, 4, 5), and
(b) shows the absolute errors around the transition band for the total ﬁlter
responses (target is a ﬂat curve in −20 dB).
the shape of the ﬁlter slope changes when the ﬁlter order is increased. Figure 5.4(b) shows the absolute errors around the transition band (300 Hz),
when the ﬁlters of different order (colored curves) are summed with the
target ﬁlter (black curve). The target curve lies at −20 dB. As can be
seen, the smallest peak error (approx. 1.5 dB, purple curve) occurs when
the lower band ﬁlter has three fourth-order sections P = 3. The algorithm
then chooses the number of fourth-order sections that results in the smallest peak error and moves to the next band and starts optimizing the order
of that band ﬁlter by comparing it to the already optimized ﬁlter.
5.4
Parallel Equalizer Design
The ﬁxed-pole design of parallel equalizer ﬁlters was ﬁrst introduced by
Bank [134]. Since then, it has been utilized in different applications,
such as magnitude response smoothing [135, 136], equalization of a loudspeaker-room response [135, 136], and modeling of musical instruments
[134, 137, 138].
The design is based on parallel second-order IIR ﬁlters whose poles are
set to predetermined positions according to the desired frequency resolution, which is directly affected by the pole frequency differences, i.e., the
distance between adjacent poles. This enables a straightforward design
of a logarithmic frequency resolution by placing the poles logarithmically,
which closely corresponds to that of human hearing, and hence, it is of-
44
Digital Equalizers
In
b1,0
-a1,1
-a1,2
Out
b1,1
H1(z)
d0
Figure 5.5. Structure of the parallel equalizer.
ten used in audio applications [139, 140]. The accuracy of the equalizer
gets better when the number of the poles is increased. Furthermore, the
accuracy increment can be targeted to a speciﬁed frequency area by placing more poles in that area. The poles can be set manually, as is done in
Publication VII, or automatically, as shown, e.g., in [135, 118].
Other ways to achieve logarithmic frequency resolution include using
warped ﬁlters [141] and more generalized Kautz ﬁlters [142, 143], where
the frequency resolution can be set arbitrarily through the choice of the
ﬁlter poles. Furthermore, Alku and Bäckström [144] introduced a linear
prediction method which gives more accuracy at low frequencies. The
method is based on conventional linear predictors of successive orders
with parallel structures, which consist of symmetric linear predictors and
lowpass and highpass pre-ﬁlters.
Figure 5.5 shows the block diagram of the parallel ﬁlter structure, which
consists of P parallel second-order sections (H1 (z), H2 (z), . . . , HP (z)) and
a parallel direct path with a gain d0 . Thus, the transfer function of the
parallel ﬁlter is
H(z) = d0 +
P
bk,0 + bk,1 z −1
,
1 + ak,1 z −1 + ak,2 z −2
k=1
(5.6)
where ak,1 and ak,2 are the denominator coefﬁcients of the k th second-order
section, which are derived based on the predetermined pole positions, and
bk,0 and bk,1 are the numerator coefﬁcients of the k th section, which are
optimized based on the target response.
45
Digital Equalizers
In Publication VII of this work, the ﬁlter design is done in the frequency
domain [140, 145], since the parallel ﬁlter structure is used to implement
a graphic equalizer, where the gain sliders deﬁne the target magnitude
response. The phase of the target magnitude response is then set to be
minimum phase. Furthermore, it is often useful to give different weights
to different frequencies depending on the desired gain, when the magnitude error is minimized on a decibel scale, as discussed in Publication
VII. Once the denominator coefﬁcients and the target response have been
determined, the numerator parameters can be solved using least-squares
error minimization, as shown in Section III-B of Publication VII.
This design is similar to the linear-in-parameter design of Kautz equalizers [142, 143]. In fact, the parallel ﬁlter structure produces effectively
the same results as the Kautz ﬁlter but requires 33 % fewer operations,
and has a fully parallel structure [146]. The beneﬁt of the fully parallel
structure is the possibility to use a graphics processing unit (GPU) instead
of a central processing unit (CPU) to do the ﬁltering [147]. GPUs have a
large number of parallel processors, which can also be used to perform
audio signal processing [148, 149].
5.5
Comparison of the Graphic Equalizers
Figure 5.6 shows the magnitude responses of both of the proposed graphic
equalizers, when they have been conﬁgured for Bark-bands (25 bands).
Figure 5.6(a) shows the magnitude response of the optimized high-order
graphic equalizer, and Figure 5.6(b) shows the magnitude response of the
parallel graphic equalizer (PGE). These two Bark-band graphic equalizers have the same command slider settings (round markers), which were
chosen to have ﬂat regions as well as large gain differences, both of which
are known to be difﬁcult challenges for a graphic equalizer.
The orders of the equalizers differ quite radically. Starting from the ﬁrst
Bark band of the optimized high-order design shown in Figure 5.6(a), the
number of the fourth-order sections are six, four, and three; bands from 4
to 23 have two fourth-order sections; and the last two bands have three
and ﬁve sections, respectively, yielding a total order of 244. The parallel
graphic equalizer shown in Figure 5.6(b) has 50 second-order sections,
resulting in a total order of 100.
Furthermore, the PGE design avoids the problem that typically occurs
in graphic equalizers, where the interaction between the adjacent band
46
Digital Equalizers
Magnitude (dB)
15
10
5
0
−5
−10
−15
100
1k
Frequency (Hz)
10k
(a)
Magnitude (dB)
15
10
5
0
−5
−10
−15
100
1k
Frequency (Hz)
10k
(b)
Figure 5.6. Comparison of two Bark-band graphic equalizers (25 bands). The ﬁgures
show the magnitude response of (a) the optimized high-order equalizer design
and (b) the parallel graphic equalizer design with the same command slider
positions. The round markers indicate the command positions.
ﬁlters cause errors in the magnitude response, as shown in the case of
the high-order graphic equalizer in Figure 5.4(b). This problem is avoided
because the parallel second-order sections are jointly optimized, as discussed in Publication VII. Comparing the PGE to the high-order graphic
equalizer, the main difference between these two equalizers is that the
joint optimization in the PGE enables smaller ﬁlter orders than the highorder graphic equalizer requires.
One downside of the PGE is that every time a command slider position
is altered by the user, the optimization has to be recalculated, whereas in
the optimized high-order equalizer, the optimization is done only once, ofﬂine, and when the user changes the command setting of a band, only the
fourth-order sections of that band must be recalculated. However, saving
preset equalizer command settings for the PGE is highly efﬁcient, since
the ﬁlter optimization can be precomputed and easily stored in memory.
47
Digital Equalizers
Both of these graphic equalizer designs can be widely used in audio signal processing applications. The high-order graphic equalizer results in
more brick-wall-like ﬁlters than the smoother PGE due to the high ﬁlter
order, as can be seen in Figure 5.6, especially around 400–1000 Hz and 2–
3 kHz. This feature makes the high-order graphic equalizer suitable, e.g.,
for the perceptual equalization applications presented in Publications II
and III.
48
6. Summary of Publications and Main
Results
This section summarizes the main results presented in the publications.
Publication I: "Signal Processing Framework for Virtual Headphone
Listening Tests in a Noisy Environment"
Publication I describes a signal processing framework that enables virtual listening of different headphones both in quiet and in virtual noisy
environments. Ambient noise isolation of headphones has become an essential design feature, because headphones are increasingly used in noisy
environments when commuting. Thus, it is important to evaluate the performance of the headphones in an authentic listening environment, since
the headphones together with the ambient noise deﬁne the actual listening experience. The proposed framework provides just this by emulating
the characteristics of the headphones, both the frequency response of the
driver and the ambient sound isolation of the headphone, based on actual
measurements. Furthermore, the proposed framework makes otherwise
impractical blind headphone comparisons possible.
Section 3 presents the calibration method of the reference headphones
used during the listening, since the frequency response of the reference
headphones must be compensated. This is done by using linear prediction ﬁlter coefﬁcients to invert the measured frequency response of the
reference headphones with the help of a peak reduction technique, also
described in Section 2.5 of this overview part. Section 4 shows how the
proposed framework is implemented using Matlab with Playrec and how
the measured impulse responses are used as FIR ﬁlters to simulate the
characteristics of the headphones.
49
Summary of Publications and Main Results
Publication II: "Perceptual Frequency Response Simulator for Music
in Noisy Environments"
Publication II further tackles mobile headphone listening and the difﬁculties caused by ambient noise. Publication II expands on the fact that the
listening environment drastically affects the headphone listening experience, as discussed in Publication I. A real-time perceptual simulator for
music listening in noisy environments is presented. The simulator uses
an auditory masking model to simulate how the ambient noise alters the
perceived timbre of music. The auditory masking model is described in
Section 3, which includes a masking threshold calculation (Sec. 3.1) and
a partial masking model (Sec. 3.2) derived from a listening test with complex test sounds resembling musical sounds. Section 4 describes the implementation of the simulator, whose output is a music signal from which
all components masked by the ambient noise as well as the noise itself are
suppressed. This is done with a high-order Bark-band graphic equalizer,
which is controlled by the psychoacoustic masking estimations. Furthermore, Section 5 presents the simulator evaluation results obtained from
a listening test.
Publication III: "Perceptual Headphone Equalization for Mitigation of
Ambient Noise"
Publication III uses the ideas and techniques developed in Publication
II to enhance the listening experience in mobile headphone listening by
equalizing the music. This is achieved by using the same psychoacoustic models that were used in Publication II to analyze ambient noise and
music while simultaneously considering the characteristics of the headphones. This enables the estimation of the ambient noise and music level
inside the ear canal. The ideal goal is to estimate the level to which the
music should be raised to in order to have the same perceived tonal balance in a noisy mobile environment as in a quiet one. The same high-order
Bark-band graphic equalizer as used in Publication II was utilized.
Section 3 together with Figures 1 and 2 describe the implementation of
the proposed perceptual headphone equalization. One of the main advantages of the proposed equalizer is that it retains a reasonable SPL after
equalization, since the music is boosted only at those frequencies where
needed. Thus, the system preserves the hearing of the user, when com-
50
Summary of Publications and Main Results
pared to the typical case where the user just raises the volume of the
music. The Matlab/Playrec implementation is also presented in Section 3.
Publication IV: "Digital Augmented Reality Audio Headset"
Publication IV introduces a novel digital design of an ARA headset. The
ARA system has been commonly believed to require the delay in the processing chain to be as small as possible, since otherwise the perceived
sound can be colored by the comb-ﬁltering effect. This is the reason why
earlier ARA systems have been implemented with analog components.
However, the in-ear headphones used in this work provide a high isolation of the outside ambient sounds, which is beneﬁcial with regard to the
comb-ﬁltering effect. Section 2 describes the physics behind the ARA technology as well as introduces possible applications to be used on the ARA
platform. Sectiond 3 and 4 discuss the delays introduced by digital ﬁlters
and passive mechanisms. Section 5 introduces the ARA equalizer ﬁlter
design, which is implemented using a DSP evaluation board, as discussed
in Section 6. Additionally, Section 6 presents how the measurements of
the properties of the DSP board are made as well as a detailed analysis
of the comb-ﬁltering effect. Moreover, a listening test was conducted to
subjectively evaluate the disturbance of a comb-ﬁltering effect with music
and speech signals. The digital implementation brings several beneﬁts
when compared to the old analog realization, such as the ease of design
and programmability of the equalizer ﬁlters.
Publication V: "Live Sound Equalization and Attenuation with a
Headset"
It was possible to implement a novel idea, where the user can control
their surrounding soundscape during a live concert, based on the successful implementation of the digital augmented reality headset presented in
Publication IV. Publication V presents a system, called LiveEQ, which
captures ambient sounds around the user, provides a user-controllable
equalization, and plays back the equalized ambient sounds to the user
with headphones. An important feature of the LiveEQ system is that it
also attenuates the perceived live music, since it exploits the high attenuation capability of in-ear headphones to provide hearing protection.
The system was implemented using Matlab with Playrec as well as with
51
Summary of Publications and Main Results
a DSP evaluation board. The Matlab implementation serves as a simulator whereas the DSP board implementation can be used in real time.
Section 4.1 further examines the comb-ﬁltering effect occurring in a hearthrough system that uses attenuative headphones.
Publication VI: "Optimizing a High-Order Graphic Equalizer for
Audio Processing"
Publication VI presents a new optimization algorithm for the high-order
graphic equalizer used in Publications II and III. The main idea is to
optimize the ﬁlter orders to improve the accuracy and efﬁciency of the
equalizer. Every practical ﬁlter has a transition band, and in the case
of a graphic equalizer, each transition band interacts with its neighboring ﬁlters creating errors in the magnitude response. The proposed iterative algorithm minimizes these errors by optimizing the ﬁlter orders,
which affects the shape of the transition band. The optimum shape of
the transition band depends on the shape of the adjacent ﬁlter. The order
optimization is executed only once ofﬂine, and after that, during the operation, only the gains of the graphic equalizer are changed according to
the user’s command settings. The order optimization was shown to reduce
the peak errors around the transition bands while the equalizer still had
a lower ﬁlter order than non-optimized equalizers with larger errors.
Publication VII: "High-Precision Graphic Parallel Equalizer"
Publication VII introduces an accurate graphic equalizer whose design
differs from that of the high-order equalizer used in Publication VI. The
novel graphic equalizer is based on the parallel ﬁlter structure introduced
in Section 5.4 of this overview. A typical problem with graphic equalizers
is that the interaction between the adjacent ﬁlters, which can result in
substantial errors especially with large boost or cut command settings.
The advantage of the parallel design is that the ﬁlters are jointly optimized, and thus, the typical errors due to interaction of the ﬁlters are
avoided. This enables the implementation of a highly accurate graphic
equalizer. It was discovered that when there are twice as many pole frequencies compared to the number of the equalizer bands, the maximum
error of the magnitude response of the proposed ±12-dB parallel graphic
equalizer is less than 1 dB across the audible frequencies.
52
7. Conclusions
This work explores equalization techniques for mobile headphone usage,
including equalization of typical headphones as well as equalization of a
hear-through headset. The mobile use of headphones has introduced new
problems in music listening by degrading the perceived sound quality of
the music. This is due to the ambient noise that leaks through the headphones into the ear canal. The ambient noise masks parts of the music
and thus changes its perceived timbre. This is the reason why a good isolation capability is nowadays an important feature in mobile headphones.
The degrading effect of ambient noise can be mitigated by using an intelligent equalization that enhances the music in those frequency regions
where the auditory masking is pronounced.
The ample isolation capability can sometimes be too much, especially
in social situations. An ARA headset is a device that aims to produce
a perfect hear-through experience with the possibility to include virtual
sounds. The ambient sounds are captured with binaural microphones and
reproduced with the headphone drivers. This requires equalization to
compensate the alterations that the headset introduces to the acoustics
of the outer ear.
This overview of the dissertation presented background information that
supports the included publications. The discussed topics include headphone acoustics and their measurements, an introduction to hear-through
systems, as well as augmented reality audio and its applications. We also
discussed digital equalizers and the matter of auditory masking, including the concepts of masking threshold and partial masking.
Publication I presents a signal processing framework for virtual headphone listening tests, which includes the simulation of headphones’ ambient noise isolation characteristics. Publication II provides a simulator
tool that helps one understand the auditory masking phenomenon in mo-
53
Conclusions
bile headphone listening. Publication III is a sequel to Publication II,
where the discovered results were utilized in an implementation of a perceptual equalizer for mobile headphone listening. Publications IV and
V concentrate on hear-through applications. Publication IV presents the
digital ARA headset, whereas Publication V presents a user-controllable
hear-through application for equalizing live music and limiting the sound
exposure during live concerts.
The parts related to Publications II–V that could be expanded in the
future are the usability and validation tests. Both of the systems introduced in Publications II and III would probably be more accurate should
they be ﬁne-tuned based on listening tests conducted in laboratory conditions as well as in real-life everyday situations. However, both of the
proposed systems operate well as they are, and the perceptual equalizer
introduced in Publication III provides an enhanced listening experience
in noisy environments, while at the same time sustains restrained SPL.
Moreover, the main issue in hear-through applications presented in Publications IV and V is the comb-ﬁltering effect. However, it should also be
tested how disturbing the comb-ﬁltering effect actually is, when the user
wears the hear-through system over long, continuous periods of time in
different environments.
Publication VI presents an optimization algorithm for a high-order graphic equalizer that iteratively optimizes the order of adjacent band ﬁlters.
Publication VII presents a novel high-precision graphic equalizer, which
is implemented using parallel second-order ﬁlters. The optimized highorder graphic equalizer enables brickwall-like ﬁlter responses, as demonstrated in Figure 5.3, which gives good accuracy for each band ﬁlter as
well as for the whole equalizer, since the overlapping of the adjacent band
ﬁlters are minimized. However, the ﬁlter orders must be quite large in
order to create the brickwall ﬁlters. The PGE in Publication VII, on the
other hand, avoids the ﬁlter overlapping issue by jointly optimizing the
second-order sections. The resulting total order of the PGE can be signiﬁcantly lower than that of the high-order equalizer; however, the PGE
cannot create as steep band ﬁlter responses as the high-order equalizer,
but results in a smoother overall magnitude response. Furthermore, the
parallel structure of the graphic equalizer enables implementations using
a GPU, which can often outperform a CPU in such a parallelizable task.
Future research could address the following aspects and tasks that were
not possible to be included in this work due to the time and space con-
54
Conclusions
straints. First, a mobile implementation of the proposed equalization
techniques and headphone applications would be important in order to allow real-life testing of the systems. The problem is the limited computing
power of smartphones as well as the shortage of input channels (typically
only one channel). However, the computing power of smartphones is constantly increasing, and modern smartphones can even be equipped with
a GPU, which can enable utilization of parallel algorithms. Even then,
the low power consumption, and thus low complexity algorithms, is a key
requirement for smartphone applications.
Another future study could include the development of algorithms reducing the comb-ﬁltering effect in hear-through devices, which should be
possible, since the equalized sound can be independently processed from
the leaked sound. Furthermore, different hardware conﬁgurations should
also be tested, since low-delay digital signal processors are widely available and they could reduce the disturbance of the comb-ﬁltering effect.
Of course, the ideal goal would be to implement all the proposed methods and more into a single pair of headphones. For example, it could
include a user-controllable frequency response (arbitrary or based on existing measured responses), which could then be enhanced with an intelligent adaptive equalizer that keeps the perceived frequency response constant no matter what kind of noisy environment the user is in. Furthermore, the hear-through function should be fully adjustable from transparent hear-through to ampliﬁcation and all the way to the most effective
ANC-driven isolation. The adjusting of the hear-through can be automated based on the ambient noise and the content the user is listening
to. Furthermore, it is possible to utilize other data provided by the user’s
smartphone, such as location, network connections (WIFI and Bluetooth),
time of day, and incoming phone calls, to control the properties of the
headphones.
It would also be useful in the future to measure a vast set of headphones,
build a database, as well as implement and publish a listening test framework on the Internet that would be available to all who are interested in
headphones. Based on the informal feedback from the users of the headphone simulator presented in Publication I, this appears to be a highly
desired application for consumers.
In the future, headphones could and probably will be used in assistive
listening applications, where the idea is to improve the hearing of people with normal hearing in different situations. I believe that assistive
55
Conclusions
listening applications will also create a need for novel signal processing
methods and increase the usage of headphones in the future.
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