IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 11, NO. 2, MARCH 2003

109

The Fundamental Limitation of Frequency
Domain Blind Source Separation for
Convolutive Mixtures of Speech
Shoko Araki, Member, IEEE, Ryo Mukai, Member, IEEE, Shoji Makino, Senior Member, IEEE, Tsuyoki Nishikawa,and Hiroshi Saruwatari, Member, IEEE

Abstract—Despite several recent proposals to achieve blindsource separation (BSS) for realistic acoustic signals, the separation performance is still not good enough. In particular, when
the impulse responses are long, performance is highly limited. In
this paper, we consider a two-input, two-output convolutive BSS
problem. First, we show that it is not good to be constrained by, whereis the frame length of the DFT
the condition
and is the length of the room impulse responses. We show that
there is an optimum frame size that is determined by the trade-off
between maintaining the number of samples in each frequency
bin to estimate statistics and covering the whole reverberation. We
also clarify the reason for the poor performance of BSS in long reverberant environments, highlighting that the framework of BSS
works as two sets of frequency-domain adaptive beamformers.
Although BSS can reduce reverberant sounds to some extent likeadaptive beamformers, they mainly remove the sounds from thejammer direction. This is the reason for the difficulty of BSS inreverberant environments.
Index Terms—Blind source separation, convolutive mixture,frame size, frequency domain, independent component analysis,reverberant speech.

I. INTRODUCTION

B

LIND source separation (BSS) is an approach to estimate
using only the information
original source signals
observed in each input channel. Thisof mixed signals
technique is applicable to the realization of noise robust speech
recognition and high-quality hands-free telecommunication
systems. It may also become a cue for auditory scene analysis.
To achieve BSS of convolutive mixtures, several methods
have been proposed [1], [2]. Some approaches consider unas FIR filters, and estimate those filters
mixing systems[3], [4]; other approaches transform the problem into thefrequency domain to solve an instantaneous BSS problem forevery frequency simultaneously [5], [6]. There are a few applications of BSS to mixed speech signals in realistic acousticalenvironments [7], but the separation performance is still notgood enough [8].
Manuscript received September 5, 2001; revised November 11, 2002. The
associate editor coordinating the review of this manuscript and approving it for
publication was Dr. Walter Kellermann.
S. Araki, R. Mukai and S. Makino are with the NTT Communication Science Laboratories, NTT Corporation, Kyoto 619-0237, Japan (e-mail: shoko@
cslab.kecl.ntt.co.jp; ryo@cslab.kecl.ntt.co.jp; maki@cslab.kecl.ntt.co.jp).
T. Nishikawa and H. Saruwatari are with the Graduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, Japan(e-mail: tsuyo-ni@is.aist-nara.ac.jp; sawatari@is.aist-nara.ac.jp).
Digital Object Identifier 10.1109/TSA.2003.809193

In this paper, we consider a two-input, two-output BSS
problem of convolutive mixtures of speech in the frequency
domain. The intention of this paper is to clarify the reason why
the performance of frequency-domain BSS in long reverberant
environments is poor. First, we show that it is not good to be, whereis the frame
constrained by the conditionis the length of the room impulse
length of the DFT and
responses. We also clarify the reason for the poor performanceof BSS in long reverberant environments, showing that theframework of BSS works as two sets of frequency-domain
adaptive beamformers.
In Section II, we summarize the framework of frequency-domain BSS for convolutive mixtures. In Section III, we explain
the relationship between the frame size and the length of theroom impulse responses . In Section IV, we investigate how
to choose the frame size for BSS of convolutive mixtures, andshow that a longer frame size is not suitable even for long reverberation [9]. In Section V, we discuss the existence of an
optimum frame size for frequency-domain BSS. Moreover, we
also clarify the reason for the poor performance of BSS in highly
reverberant environments. We point out that frequency-domain
BSS works as two sets of frequency-domain adaptive beamformers [10]. Cardoso and Souloumiac [11] indicated the connection between blind identification and beamforming in a narrowband context. We discuss this relationship more closely, andprovide a physical understanding of frequency-domain BSS. Although BSS can reduce reverberant sounds to some extent [12]like adaptive beamformers, it mainly removes sound from thejammer direction. This understanding explains the reason forthe difficulty of BSS in reverberant environments. Section VI
concludes this paper.
II. FREQUENCY DOMAIN BSS OF
CONVOLUTIVE MIXTURES OF SPEECH
The

signals recorded by

microphones are given by(1)

is the received
where is the source signal from a source ,is a -point impulse response
signal by microphone , andfrom source to microphone . In this paper, we consider a twoinput, two-output convolutive BSS problem, i.e.,(Fig. 1).

1063-6676/03$17.00 © 2003 IEEE

110IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 11, NO. 2, MARCH 2003

Fig. 1. BSS system configuration.

The frequency-domain approach to convolutive mixtures isto transform the problem into an instantaneous BSS problem inthe frequency domain [5], [6]. Using -point short-time Fouriertransformation for (1), we obtain(2)
represents the time-depenwhere denotes the frequency,
dence of the short-time Fourier transformation,is the source signal vector, andis the observed signal
does not depend on time .
vector. The mixing matrix
is invertible,
We assume that the (2 2) mixing matrix.
and that
The unmixing process can be formulated in a frequency bin

Fig. 2. Layout of a room used in experiments.

IV. EXPERIMENTS
A. Conditions for Experiments
1) Learning Algorithm: For the calculation of the unmixing
in (3), an algorithm based on the minimization
matrixof the Kullback-Leibler divergence [6], [16] has been proposed.
is obtained by using the following iterative
The optimal
equation:(4)(3)
is the estimated
whererepresents a (2 2) unmixing
source signal vector, andis determatrix at frequency bin . The unmixing matrix
andbecome mutually indemined so thatpendent. The above calculation is carried out at each frequency
independently.
III. FRAME SIZE FOR BSS OF CONVOLUTIVE MIXTURES
It is commonly believed that the frame size must be longer
than to estimate the unmixing matrix for a -point room impulse response. In this paper, we consider that the DFT frame
size is equal to the length of unmixing filter .
The reason for the belief that the frame size must be longer
than is because: i) A linear convolution can be approximated. Therefore, in order toby a circular convolution if
must be held. ii)
transform (1) into (2) accurately,
Signal separation by using a noise cancellation framework withsignal leakage into the noise reference was discussed in [13],[14]. In the case of a noise canceller, we should use an FIR; therefore,. iii) If we want tofilter of length
estimate the inverse system of a system with impulse response-taps long, we need an inverse system that is -taps long,; therefore,.
where
in order to reIn [8], [15], however, the authors used
to assure that frame
duce permutation inconsistency andsize of the DFT is computed over a stationary window of data.
It is important to clarify what filter length is suitable and also
why that filter size is suitable for BSS of convolutive mixtures.
We investigate this in Section IV and discuss our findings in
Section V.

,
denotes the averaging operator, is
whereused to express the value of the th step in the iterations, andis the step size parameter. In addition, we define the nonlinear
asfunction(5)
andare the real part and the imaginary part ofwhere, respectively.
For more stable and faster convergence, Amari [17] proposedan algorithm based on the natural gradient. Using the natural
gradient, we get the following iterative equation:(6)in (3), we used
For the calculation of unmixing matrix
this iterative equation (6).
2) Conditions for Experiments: Separation experiments
were conducted using speech data convolved with impulse
responses recorded in three environments specified by different
ms, 150 ms, and 300 ms. Since
reverberation times:
the sampling rate was 8 kHz, 150 ms, and 300 ms corresponds
taps and 2400 taps, respectively. As the original
tospeech, we used two sentences spoken by two male and two
female speakers. The investigations were carried out for six
combinations of speakers.
The layout of the room we used to measure the impulse responses is shown in Fig. 2. We used a two-element array with aninter-element spacing of 4 cm. The speech signals arrived fromtwo directions, 30 and 40 . An example of a measured room
impulse response used in our experiments is shown in Fig. 3.

ARAKI et al.: FUNDAMENTAL LIMITATION OF FREQUENCY DOMAIN BLIND SOURCE SEPARATION

Fig. 3. Example of (a) measured impulse response hand (b) its energy decay curve. T = 300 ms.

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used in experiments

Fig. 3(b) shows the energy decay curveof an impulse re, which can be obtained by integrating the energy ofsponseimpulse response as follows:

The reverberation timeis defined as the time for an energy
attenuation of 60 dB.
In these experiments, we varied the frame size from 32 to
2048 and investigated the performance for each condition. The
frame shift was half of the frame size , the analysis window
was a Hamming window, and the step size for adaptation was. The learning ofusing (6) was iterated
until the adaptation converged.
In frequency-domain BSS, a scaling and permutation
problem occurs, i.e., the estimated source signal components
are recovered with a different order and gain in the different
frequency bins. To solve this problem, we used the blind
beamforming algorithm proposed by Kurita et al. [16]: first,
we estimate
from the directivity pattern obtained byso that thethe source directions and reorder the row of
directivity pattern forms a null toward the same direction in all
so that thefrequency bins, then we normalize the row ofgains of the target directions become 0 dB.
3) Evaluation Measure: In order to evaluate the performance for different frame sizes with different reverberation, we used the signal-to-interference ratio (SIR),
times
defined as follows:
SIR

SIR

SIR

SIR

(7)

SIR

(8)

Fig. 4. Results of SIR for different frame sizes: (a) explanation of the test data
conditions, (b) nonreverberant test, (c) reverberant test (T = 150 ms), and (d)reverberant test (T = 300 ms).

where
and
. SIR means the ratio ofa target-originated signal to a jammer-originated signal. These
values were averaged over all six combinations of the speakers,
and SIR and SIR were averaged.
B. Experimental Results
The experimental results are shown in Fig. 4. First we explain
the test data condition in Fig. 4(a) as follows; i) The lengths ofthe mixed speech signals were about 8 s each, the beginning 3 sof the mixed data were used for the learning according to (6),and the entire 8 s data was separated (dashed lines); ii) The entire
8 s of the mixed data for the learning, and the entire 8 s data forthe separation (thick lines); iii) The lengths of the mixed data
were 15 s, the lengths of the learning data were the beginning
8 s, and the entire 15 s data were separated (thin lines). Notethat in cases i) and iii), the separation evaluation was executedfor “open” data, i.e., the separated data was not the same as
the learning data, and in case ii), the evaluation was done for“closed” data, i.e., the separated data was exactly the same as
the learning data.

112IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 11, NO. 2, MARCH 2003

In the case of 3-s learning (dashed lines), we obtained the best
performance with a short frame size both in nonreverberant tests[Fig. 4(b)] and in reverberant tests [Fig. 4(c) and (d)]. A shortframe was found to function far better than a long frame size,
even for long room reverberation.
For the longer learning data, i.e., the 8-s data, the results wereslightly different from the 3-s learning case. In this case, we obtained a better separation performance than for the 3-s learningcase. Furthermore, in comparison with the 3-s learning case, thebest performance was realized when we used a longer frame size
. With an overly long frame size, however, the performance
was poor even when we used longer learning data.
is notEven for long room reverberation, the condition
suitable, and a shorter frame size is best. We will discuss thisin the next section.
Moreover, we achieved better performance in “closed” tests
than in “open” tests. Since the room impulse response was notchanged during the entire experiment, there is little difference
between the closed and open tests. If the room impulse response
were to slightly change during a real recording, however, thedifference there would be larger between the closed and open
tests.

Fig. 5. Average Frobenius norm of off-diagonal terms of h8(Y )Y i. The
solid lines refer to data of 8 s, and the dashed lines refer to data of 3 s. No
reverberation (T = 0 ms).

dence, i.e., if this term is small, two signals should be highly
independent.
The independence measure is the Frobenius norm of
averaged over frequency, defined as follows:

V. ANALYSIS AND DISCUSSION(11)

A. Optimum Frame Size for Frequency-Domain BSS
In the previous section, we showed that a longer frame size
fails even for long room reverberation. In this section, we
discuss the reason why both a short frame and a long frame fail.
1) The Case of a Long Frame: In the frequency-domain BSS
but.
framework, the signal we can use is not
is long, the number of samples in eachIf the frame size
frequency bin is small. This causes assumptions to collapse,
like the zero-mean and independence assumptions, because thismakes the correct estimation of statistics difficult. In this paper,
when the number of samples is too small to estimate statistics
correctly, we say “the independence assumption is not held” or“the independence decreases/collapses.” As an example of sucha collapse, we observe that the independence of two source signals goes down when the frame size becomes longer. In order toevaluate the independence of two signals from different pointsof view, we used two measures. One was the Frobenius norm of
the adaptation term in (6), and the other was a correlation coefficient.
: To investigate
Measure 1: Off-Diagonal of
the independence between the two signals, we used the bracketed term of the adaptation equation (6) to define a new measure.
Here, we set this term as

Fig. 5 shows this measure for each frame size . As the meaandshows, the independence desure for source signals
creases when the frame size becomes longer. In these cases,
because the number of data samples is smaller, the assumptionof independence does not hold for the two source signals. Thisis a reason why the long frame fails. Besides, the independence
is higher when we use 8-s long learning data than when we use
3-s data.
Measure 2: Correlation Coefficient: For a second measure of the independence, we used the correlation coefficient.
Although a correlation coefficient does not show independence
directly, we use this measure as an index of independence. The
independence measure here is the average of the correlation coefficients over all frequency bins
(12)
where

(9)
(10)
,
and represents
wherethe source signal , observed signal or separated signal .
becomes the zero maWhen the algorithm converges,
trix, and the two signals become mutually independent. Therefore, we can consider this term as a measure of the indepen-(13)
represents a mean value, and is the source signal , observed signal, or separated signal .
Fig. 6 shows the relationship between the frame size and theaverage correlation coefficient. The decrease of independence
is observed again for the source signals when the frame size
becomes longer. Moreover, the independence is also higher with
8-s learning data, than with 3-s data.

ARAKI et al.: FUNDAMENTAL LIMITATION OF FREQUENCY DOMAIN BLIND SOURCE SEPARATION

Fig. 6. Relationship between T and the average correlation coefficient. The
solid lines refer to data of 8 s, and the dashed lines refer to data of 3 s. T =
0 ms.

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Fig. 7. Definition of system for target and jammer. Signal s is a target andsignal s is a jammer. (a) System for the target. (b) System for the jammer.

Because the number of samples in each frequency bin becomes smaller when we use a longer frame, the assumption of
independence does not hold for the two source signals. This isa reason why the long frame fails.
Moreover, when the filter length becomes longer, the numberof coefficients to be estimated increases while the number ofsamples for learning in each frequency bin decreases. As aresult, the estimation error is integrated and the performance
worsens. This is another reason for poor performance when weuse a longer frame.
2) The Case of a Short Frame: In our experiments, a shorter
frame also failed. When we use a short frame, the frame could
not cover the reverberation; therefore, the separation performance was limited.
In order to show the reverberation coverage of a frame, weinvestigated the impulse response of the mixing and unmixing
systems in the time domain [12]. We considered a separated
signal , target signal , and jammer signal . When the target
is an impulse
and jammer signal, we can meaof the system for the target signal
sure the impulse responseand, we can[Fig. 7(a)]. Similarly, whenof the system for the jammer
measure the impulse response[Fig. 7(b)]
(14)
(15)
determines the remaining reverberaThe impulse responsetion sound of the jammer that cannot be reduced using BSS,
and that worsens the separation performance. Therefore we pay
attention to .
forFig. 8 shows the jammer signal’s impulse response
and 512 and forms. Fig. 8(a)is the observed signal at microphone 1 when the target signal, i.e., the impulse response. In the case of[Fig. 8(b)], the length of the unmixing system is much shorter
than the length of the reverberation; accordingly, reverberation
longer than the frame cannot be reduced. On the other hand,[Fig. 8(c)], the reverberation covered by thewhenframe size is reduced.

Fig. 8.

Impulse response of system for jammer h .

From Section V-A-1 and Section V-A-2, we conclude thatin frequency-domain BSS, there is an optimum frame size determined by a tradeoff between maintaining the assumption of
independence and covering the whole reverberation interval.
B. Length of Learning Data and Separation Performance
In Section IV-B, we obtained better performance using 8-sdata than using 3-s data. The reason for this result is also explained by Figs. 5 and 6. With 8-s data, the independence was

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 11, NO. 2, MARCH 2003

Fig. 10. Relationship between the contribution of a direct sound and theseparation performance. T = 300 ms, T = 512. (a) Example of an impulse
response. (b) Energy decay curve. (c) Separation performance.

In the BSS framework, what an unmixing matrix
canbecomes a
do is to minimize the second term of (6), andsolution of(16)

Fig. 9. Directivity patterns obtained by (a) NBF, (b) BSS (T = 0 ms), (c)BSS (T = 150 ms), and (d) BSS (T = 300 ms). Frame size T = 256, 3-slearning.

better maintained than with 3-s data. Therefore, we can obtainbetter performance using the longer data. Furthermore, the optimum frame size changes when we use learning data of different length, because the optimum frame size is determined by
the trade-off we mentioned in Section V-A.
C. Physical Understanding of Frequency-Domain BSS
can at best
It is well known that an unmixing matrix
be obtained up to a scaling and a permutation. Before the permutation and scaling problem, however, we must note that the
BSS algorithm cannot solve the dereverberation/deconvolution
problem in itself [14].

andare arbitrary complex constants. This means
whereis in general not the inverse matrix of the mixingthatassystem . We can understand this unmixing system
two sets of microphone array systems, i.e., two sets of adaptive
beamformers (ABFs) [10].
We can form only one null toward the jammer in the case of
two microphones. Fig. 9 shows directivity patterns obtained bya null beamformer (NBF) and BSS; Fig. 9(a) is obtained by an
NBF that forms a steep null directivity pattern toward a jammer
under the assumption of the jammer’s direction being known.
, (c)Fig. 9(b)–(d) are obtained by BSS for (b)ms, and (d)ms. When, a sharp null is obis long, the directivity patterntained like with an NBF. Whenis comparatively duller; however, we can still draw a directivity
pattern. Although BSS and ABF can reduce reverberant sounds
to some extent (see Fig. 8) [12], they mainly remove sound fromthe jammer direction. This understanding clearly explains thepoor performance of BSS in a real room with long reverberation.
Fig. 10 shows the performance when the contribution of thedirect sound is changed artificially. The performance increases
with the increase of the contribution of the direct sound. This isARAKI et al.: FUNDAMENTAL LIMITATION OF FREQUENCY DOMAIN BLIND SOURCE SEPARATION

the same characteristic as that of an ABF. Because an unmixing
mainly removes sound from the jammer direcsystemtion, chiefly the direct (largest) sound of the jammer can be separated, and the other reverberant components which arrive fromdifferent directions cannot be separated. As a result, separation
performance is fundamentally limited.
Moreover, as we have shown in Section IV, a long frame
size works poorly in the frequency-domain BSS for speech
data of a few seconds. This is because when we use a long
frame, the assumption of independence between
anddoes not hold at each frequency; this is caused
by a small number of samples in each frequency bin. ABF,
however, does not use the assumption of independence, so it
can achieve good performance using a long frame size, i.e.,high frequency resolution. Therefore, the performance of BSS
is upper bounded by that of ABF. Note that ABF needs to know
the array manifold and the target direction. Note also that ABF
can be adapted only when a jammer exists but a target does
not exist, whereas BSS can adapt in the presence of target andjammer, and also in the presence of only one active source.
The BSS was shown to outperform an NBF [18], [12]. It is
well known that an ABF outperforms an NBF in long reverberation. Our understanding also clearly explains this.
VI. CONCLUSIONSIn this paper, we discussed why the separation performanceof frequency-domain BSS is poor when there is long reverberation. First, we showed that it is not good to be constrained by the, where is the frame size of the FFT and iscondition
the length of a room impulse response. This is because the lackof data causes the collapse of the assumption of independence
between the two source signals in each frequency bin when thedata length is short, or when a longer frame size is used. On
the other hand, when we use a short frame, we cannot get good
performance, because long reverberation cannot be covered bya short frame. Therefore, there is an optimum frame size determined by a trade-off between maintaining the assumptionof independence and covering the whole reverberation in frequency-domain BSS.
Next, we showed a physical understanding of frequency-domain BSS. We can understand the frequency-domain BSS
system as two sets of microphone array systems i.e., two sets ofadaptive beamformers (ABFs). Because ABF and BSS mainly
consider sound from the jammer direction by making a null
toward jammer, the separation performance is fundamentally
limited. Furthermore, we can conclude that the performance of
the BSS is upper bounded by that of ABF.
This understanding clearly explains the poor performance of
BSS in a real room with long reverberation.
ACKNOWLEDGMENT
The authors thank Dr. S. Katagiri and Dr. K. Shikano for
their continuous encouragement. They also thank Dr. F. Asanofor detailed discussions. The authors are grateful to the associate editor and the anonymous reviewers, whose constructive
and helpful comments led to significant improvements in the
manuscript.

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[17] S. Amari, “Natural gradient works efficiently in learning,” NeuralComput., vol. 10, pp. 251–276, 1998.
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Shoko Araki (M’01) received the B.E. and M.E. degrees in mathematical engineering and information
physics from the University of Tokyo, Tokyo, Japan,
in 1998 and 2000, respectively.
In 2000, she joined NTT Communication Science
Laboratories. Her research interests include digital
signal processing, array signal processing and blindsource separation applied to speech signals.
Ms. Araki is a member of the Acoustical Societyof Japan (ASJ).

Ryo Mukai (A’95–M’01) received the B.S. and M.S.
degrees in information science from the University ofTokyo, Tokyo, Japan, in 1990 and 1992, respectively.
He joined NTT in 1992. From 1992 to 2000,
he was engaged in research and development ofprocessor architecture for network service systems
and distributed network systems. Since 2000, he has
been with NTT Communication Science Laboratories, where he is engaged in research of blind source
separation. His current research interests include
digital signal processing and its applications.
Mr. Mukai is a Member of ACM, the Acoustical Society of Japan (ASJ), andthe Information Processing Society of Japan (IPSJ).

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 11, NO. 2, MARCH 2003

Shoji Makino (A’89–M’90–SM’99) was born inNikko, Japan, on June 4, 1956. He received the B.E.,
M.E., and Ph.D. degrees from Tohoku University,
Sendai, Japan, in 1979, 1981, and 1993, respectively.
He joined the Electrical Communication Laboratory of Nippon Telegraph and Telephone Corporation(NTT) in 1981. Since then, he has been engaged inresearch and development of acoustic echo cancellation and adaptive algorithms. He is now a SeniorResearch Scientist, Supervisor, and Group Leader at
the Speech Open Laboratory of the NTT Communication Science Laboratories. His research interests include blind source separation of convolutive mixtures of speech, acoustic signal processing, and adaptive
filtering and its applications. He is the author or co-author of more than 170 articles in journals and conference proceedings and has been responsible for morethan 140 patents.
Dr. Makino received the Paper Award of the Institute of Electronics, Information, and Communication Engineers of Japan in 2002, the Paper Award of the
Acoustical Society of Japan in 2002, the Achievement Award of the Institute ofElectronics, Information, and Communication Engineers of Japan in 1997, andthe Outstanding Technological Development Award of the Acoustical Society ofJapan in 1995. He is a member of the Conference Board of the IEEE Signal Processing Society and an Associate Editor of the IEEE TRANSACTIONS ON SPEECH
AND AUDIO PROCESSING. He is a member of the Technical Committee on Audioand Electroacoustics as well as the Technical Committee on Speech of the IEEE
Signal Processing Society. He is the General Chair of the 2003 International
Workshop on Acoustic Echo and Noise Control and the Organizing Chair of the2003 International Conference on Independent Component Analysis and Blind
Signal Separation. He served on the Program Committee of the 2003 IEEE International Workshop on Neural Networks for Signal Processing, the Program
Committee of the 2004 International Congress on Acoustics, the Program Committee of the 2002 IEEE International Workshop on Neural Networks for Signal
Processing, the Organizing Committee of the 2002 China-Japan Joint Conference on Acoustics, and the Technical Committee of the 2001 and 1999 International Workshop on Acoustic Echo and Noise Control. He is a Vice Chair of theTechnical Committee on Engineering Acoustics of the Institute of Electronics,
Information, and Communication Engineers of Japan. He is a member of the
Acoustical Society of Japan and the Institute of Electronics, Information, andCommunication Engineers of Japan.

Tsuyoki Nishikawa was born in Mie, Japan, in
1978. He received the B.E. degrees in electronicsystem and information engineering from Kinki
University in 2000 and received the M.E. degrees
in information and science from Nara Institute of
Science and Technology (NAIST) in 2002. He is
now pursuing the Ph.D. degree at Graduate Schoolof Information Science, NAIST.
His research interests include array signal
processing and blind source separation.
Mr. Nishikawa is a member of the ASJ.

Hiroshi Saruwatari (M’00) was born in Nagoya,Japan, on July 27, 1967. He received the B.E., M.E.,
and Ph.D. degrees in electrical engineering from
Nagoya University, Nagoya, Japan, in 1991, 1993,
and 2000, respectively.
He joined Intelligent Systems Laboratory, SecomCo., Ltd., Mitaka, Tokyo, Japan, in 1993, where heengaged in the research and development on the ultrasonic array system for the acoustic imaging. He iscurrently an Associate Professor of Graduate Schoolof Information Science, Nara Institute of Science andTechnology. His research interests include array signal processing, blind source
separation, and sound field reproduction.
Dr. Saruwatari received the Paper Award from IEICE in 2001. He is a Memberof IEICE and the ASJ.