IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 6, NOVEMBER 2006

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Blind Extraction of Dominant Target Sources
Using ICA and Time-Frequency Masking
Hiroshi Sawada, Senior Member, IEEE, Shoko Araki, Member, IEEE, Ryo Mukai, Senior Member, IEEE, andShoji Makino, Fellow, IEEE

Abstract—This paper presents a method for enhancing target
sources of interest and suppressing other interference sources. The
target sources are assumed to be close to sensors, to have dominant powers at these sensors, and to have non-Gaussianity. The
enhancement is performed blindly, i.e., without knowing the position and active time of each source. We consider a general casewhere the total number of sources is larger than the number of
sensors, and neither the number of target sources nor the total
number of sources is known. The method is based on a two-stage
process where independent component analysis (ICA) is first employed in each frequency bin and then time-frequency masking isused to improve the performance further. We propose a new sophisticated method for deciding the number of target sources andthen selecting their frequency components. We also propose a newcriterion for specifying time-frequency masks. Experimental results for simulated cocktail party situations in a room, whose reverberation time was 130 ms, are presented to show the effectivenessand characteristics of the proposed method.
Index Terms—Blind source extraction, blind source separation(BSS), convolutive mixture, frequency domain, independent component analysis, permutation problem, time-frequency masking.

I. INTRODUCTION
HE technique for estimating individual source components
from their mixtures at sensors is known as blind source
separation (BSS) [1]–[4]. With some applications such as brain
imaging or wireless communications, it makes sense to extract
as many source components as possible, because many sources
are equally important. However, with audio applications such as
speech enhancement, the sources do not necessarily have equalsignificance. We often want to extract only a few sources that areclose to sensors, have dominant powers, and/or have interesting
features.
This paper presents a method for extracting source signalsof interest and suppressing other interference sources blindly.
Let us formulate the task. Suppose that a few target sourcesand other background sources
are convolutively mixed and observed atsensors

T(1)

Manuscript received January 27, 2005; revised October 30, 2005. The associate editor coordinating the review of this paper and approving it for publication
was Dr. Dan Ellis.
The authors are with NTT Communication Science Laboratories, NTT
Corporation, Kyoto 619-0237, Japan (e-mail: sawada@cslab.kecl.ntt.co.jp;
shoko@cslab.kecl.ntt.co.jp; ryo@cslab.kecl.ntt.co.jp; maki@cslab.kecl.ntt.co.
jp).
Digital Object Identifier 10.1109/TASL.2006.872599

Fig. 1. Signal notations.

where
(2)
is the component of
measured at sensor . The impulse response from source to sensor is denoted as. The goal,that are close to theis to have output signals
components(3)of target sources measured at a selected sensor . The task
. Theshould be performed only with the mixtures
number of target sources and the total number of sources
are unknown. The number
is assumed to be no more than
the number of sensors , and may be larger than . Fig. 1
shows the signal notations.
The first problem is how to extract target sources
blindly. Even if the total number of sources
could be larger
than , independent component analysis (ICA) [1]–[4] withassumption producescomponents that maxianmize an ICA criterion such as non-Gaussianity. We assume thatthe target sources are non-Gaussian, close to sensors, and dominant in the mixtures. Therefore, we expect that some of thecomponents produced by ICA correspond to target sources
whose ICA criteria are high.
We employ ICA in the time-frequency domain [5]–[11]. The
reason is that it is efficient [11] and also fits time-frequency
masking, which is discussed below. An additional operation thatshould be performed is the selection of each target component
in every frequency bin. This is considered to be the permutation
problem of frequency-domain BSS [12]. It has been reported
that the selection of a component with maximum kurtosis works
well when the target is speech and the interferences are babble
sources [13]. However, this does not always work well for a casewhere the interferences are also speech.

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Fig. 2. Flow of proposed method.

In order to solve the permutation problem for mixtures withmany speeches, we exploit the information of basis vectors (10)produced by ICA. Our previously reported methods estimate
the directions [12], [14] and/or the distances [14] of the sourcesfrom the basis vectors, and then cluster the estimated directions
and/or distances to solve the permutation problem. However,the system needs to know the locations of sensors to estimate
such geometric information about the sources. In Section IV,
we propose a new method for solving the permutation problem
by clustering the basis vectors themselves after some normalization. With this approach, we do not need to know the sensor
locations, simply the maximum distance between a sensor andany other sensor. This relaxation makes it easy to use a nonuniform arrangement of sensors, and also eliminates the need forsensor calibration.
The next issue is that some interference still remains in. Postthe extracted frequency components whenfiltering [13], [15] can be used to reduce such residual interference. However, it needs additional adaptation where thestep size should be controlled based on the short-term powerof the target. Another approach is time-frequency masking[16]–[22], which is efficient for sources with sparseness inthe time-frequency domain, such as speech. Time-frequency
masking has been well studied in the research area of computational auditory scene analysis [22]–[26]. The performance oftime-frequency masking depends on how well we can specify
the time-frequency slots where the target source is active. A
simple way to specify such slots is to calculate the phase and/or
amplitude difference between the observations of different sensors [16]–[18]. Another recently proposed approach involves
calculating the power ratio between an input and outputs of a
spatial filter (beamformer [20], [21] or ICA [21]). However,such a power-based criterion depends on the scaling ambiguityof ICA or beamformer outputs. In Section V, we propose a newcriterion for specifying masks. It is based on the cosine distance
between a sample vector and the basis vector corresponding
to a target. The distance is calculated in a spatially whitened
space where the target basis vector is expected to be almost orthogonal to those of interferences. Therefore, the new criterion
does not suffer from the problem of scaling ambiguity.
This paper is organized as follows. The next section provides
an overview of our proposed method. Section III discusses how
ICA can be applied to our situation and what should be done
for the ICA results. Section IV describes how basis vector clustering works to solve the permutation problem and to decide thenumber of target sources to be extracted. Section V discusses
how to specify time-frequency masks. Section VI presents experimental results, and Section VII concludes this paper.

II. OVERVIEW OF PROPOSED METHOD
Fig. 2 shows the flow of the proposed method. First, time-dosampled at frequency
are conmain observed signals
withverted into frequency-domain time-series signals
an -point short-time Fourier transform (STFT)(4)
is a frequency,
is a window that tapers smoothly to zero at each end,such as a Hanning window, and is a
new index representing time.
The following operations are performed in the frequency domain. There are two advantages to this. First, the convolutive
mixtures (1) can be approximated as instantaneous mixtures at
each frequency
where

(5)
where
is the frequency response from source to sensor, andis a frequency-domain time-series signal of
obtained by the same operation as (4). The frequency-domaincounterpart of (3) is(6)
where should be the same for all frequency bins . The second
advantage is that the sparseness of a source signal becomesprominent in the time-frequency domain if the source is coloredand nonstationary such as speech. The possibility of
being close to zero is much higher than that of.
Then, we apply ICA (Section III) to the STFT results(7)
which we call a sample vector, and obtain basis vectors
defined by (10) and independent
components(8)
Some of these independent components correspond to the components of dominant sources. However, the correspondence is
not clear at this stage because of the permutation ambiguityof ICA. Thus, basis vector clustering (Section IV) is performed to decide the number of target sources and produce
and independent components
basis vectors

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2167, whose correspondences to the target
sources are specified.
is larger than the number
If the number of total sourcesof sensors , independent components
produced by ICA have some residuals caused by the limitation of spatial filtering. Time-frequency masking (Section V) is used to reduce such residuals and to obtain outputs, which should be close to (6) in eachfrequency bin. At the end of the flow, time-domain output
are obtained by an inverse STFT (ISTFT)
signalsforFig. 3. ICA example for real instantaneous mixtures (N = 3, M = 2, Q = 1).

Once the correspondence between basis vectors and target
sources are identified, i.e., the permutation problem is solved,
we solve the scaling ambiguity in (11)

.

(13)

III. INDEPENDENT COMPONENT ANALYSIS (ICA)
To extract the components of dominant sources, we apply. Even though the total number
ICA to sample vectorsof sources may be larger than the number of sensors , weemploy ICA by assuming that the number of independent components is equal to
(9)
is an
separation matrix.
where
In the experiments shown in Section VI, we calculatedbyusing a complex-valued version of FastICA [3], [27], and improved it further by using InfoMax [28] combined with the natural gradient [29] whose nonlinear function is based on the polar
coordinate [30].
to obtain basis vectors
Then, we calculate the inverse of(10)
invertible by using an appropriate
It is not difficult to make
ICA procedure, such as whitening followed by unitary transformation (e.g., FastICA [3]). By multiplying both sides of (9) by, the sample vector
is represented by a linear combination of basis vectors(11)
By comparing (11) and the vector notation of the mixing
model (5)
(12)
where
, we observe the following fact.
Since
are assumed to be dominant non-Gaussiancorresources, it is strongly expected that some ofand thus some ofcorrespond
spond to. The correspondence between
and
as
to
well as the number of target sources are unknown at this time,
because of the permutation ambiguity of ICA. They will be
specified by basis vector clustering as described in Section IV.

for any nonzero complex scalar. This is easily solved by(14)

where is the index of the sensor specified in (6). The reason isasas follows. The goal in each frequency bin is to makedefined in (6) as possible. And we can deriveclose torelations(15)
from (6), theterm in (12), and the term in (11).
Here, we have a simple example to see how well standard ICA
isworks for such a case where the number of total sources
larger than the number of sensors , but the number of dominant sources is no more than . Fig. 3 shows some plots forreal instantaneous mixtures. The left-hand plot shows the mixtures. The square shows the mixing vector of the dominant target
source, and the two triangles show those of the other less dominant sources. The center plot shows whitened mixtures, whereseparation is not achieved. The right-hand plot shows the ICA
result, where the mixing vector of the dominant target source is
identified.
IV. BASIS VECTOR CLUSTERINGThe purpose of basis vector clustering is to solve the permutation problem and also to decide the number of dominanttarget sources. As shown in [12], integrating the basis vector
and signal envelope
information solves the permutation problem robustly and precisely, and we also employ
this approach in the experiments shown in Section VI. In therest of this section, we discuss a new method for exploiting thebasis vector information.
A. Frequency Normalization
The new method involves normalizing all basis vectors, for all frequency bins
,

,

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form a cluster for a source that is placed atbasis vectors
a specific position.
for the deLet us discuss the intention of using
nominator of (16). From the fact that, an inequality(20)
Fig. 4. Direct-path (near-field) model.

such that they form clusters, each of which corresponds
to an individual source. The normalization is performed by selecting a reference sensor and calculating(16)
where is the propagation velocity, andis the maximum
distance between the reference sensor and any sensor. Then, we apply unit-norm normalization

holds. This property is important for the distance measure (22),which will be used for the clustering algorithm. In the range
inspecified by (20), the distance between two elements
creases monotonically as the difference between two arguments
increases. If argument
falls out of thewould decrease as the difference
range (20), the distanceincreases, which would adversely affect the
distance measure (22).
B. Clustering Basis Vectors and Solving Permutation
Ambiguity,andAfter normalizing all basis vectors,, we perform a clustering
formed by normalizedalgorithm to find clusters. The centroidof a cluster
is calculated byvectors

(17)
(21)
.
for
Here, we explain why normalized basis vectors
form acluster for a source. Let us approximate the multipath mixing
model used in (1) and (5) by using a direct-path (near-field)
model (Fig. 4)

is the number of vectors in. The clustering criwhere
terion is to minimize the total sum of the squared distances
between cluster members and their centroid

(18)

(22)

is the distance between source and sensor .
wheredepends on the
We assume that the phasedistance normalized with the distance to the reference sensor .
This assumption makes the phase zero at the reference sensor .
depends on both
We also assume that the attenuation.
the distance and a frequency-dependent constant
By considering the permutation and scaling ambiguity of
ICA, a basis vector and its elements are represented as

This minimization can be performed efficiently with the
k-means clustering algorithm [31]. Some examples of clustering results are shown in Figs. 10 and 11.
Once we have foundclusters, we need to
identify clusters that correspond to dominant target sources. We decide that a clusterwith a small variancebelongs to the set of target sources. The rationale
behind this criterion is that the mixing model (18) is more valid
than for the other sources. For target sourcesfor, the direct-path components of impulse responses
are distinct since is assumed to be close to the sensors.
To identify target source clusters, we sort the clusters
so that their variances are sorted in ascending order

(19)
where
is a nonzero complex scalar representing the scaling
ambiguity, and index , which may be different from index ,represents the permutation ambiguity. Substituting (18) and (19)
into (16) and (17) yields

(23)
where
is a predefined threshold for specifying the set oftarget sources. Then, to align the permutation ambiguity of ICA,
we renumber the indexes of the basis vectors bywhich is independent of frequency, and dependent only on thepositions of the sources and sensors. Therefore, normalized(24)

SAWADA et al.: BLIND EXTRACTION OF DOMINANT TARGET SOURCES

where
ping decided for each frequency2169

is a one-to-one mapby(25)

Fig. 5. Angle  calculated in whitened space.

We also renumber independent components
accordingly.
V. TIME-FREQUENCY MASKING
A. Motivation
Let us discuss the motivation for using time-frequency
masking. Suppose that the permutation ambiguities of ICA
solutions are solved at this stage. Then, the extraction of adominant source by ICA (9) is represented by(26)

Fig. 6. Masking functions (30) with three sets of parameters ( ; g ).

being close to zero is very high, (12) can be approximated as

(27)
(31)
If
,
satisfies,
and makes thesecond term zero. However, we assume that the total numberof sources is generally larger than . In this case, there
such thatexists a set,. Thus,contains unwanted residuals. The purpose of time-frequency maskingthat contains lessis to obtain another version of output
than.
power of the residuals. Let uswhere depends on each time-frequency slot
consider the whitened-space counterpart of (31), while distinguishing between cases where is the only active source (32)and other cases (33)
(32)
(33)

B. Proposed Procedure
Time-frequency masking is performed by
(28)
where
is a mask specified for each time-fre. We specify masks based on the angle
quency slot
betweenandcalculated in the space transformed
by a whitening matrix,.
be whitened samples andLetbe the basis vector in the whitened space. The angle
is calculated by

If the number of sources is equal to or less than the number ofin the whitened space aresensors , vectors
orthogonal to each other. Even if, the vectorof a dominant source , which points in almost the same direc, tends to have large angles with the other vectors
tion as. Fig. 5 (and also Fig. 3)
shows such a case. Therefore, calculating the angle (29) prois the only active
vides information about whether or not, and specifies the corresource at a time-frequency slot
accordingly.
sponding mask
VI. EXPERIMENTS(29)
A. Experimental Conditions and Evaluation Measuresfor each time-frequency slot (Fig. 5). Then, we calculate a mask
by using a logistic function [26] (Fig. 6)(30)
and are parameters specifying the transition point
where
becomes smaller, theand its steepness, respectively. As
residual power that appears indecreases, but the musicalnoise in increases.
The effectiveness of the above operation depends on thesparseness of sources. If we assume that the possibility of

We performed experiments to enhance dominant speeches
that were close to microphones. We measured impulse reunder the conditions shown in Fig. 7. The
sponses
speaker positions simulated a cocktail party situation. Mixturesat the microphones were made by convolving the impulse
responses and 6-s English and Japanese speeches sampled at 8
, whose arrangement
kHz. We used four microphones
was three-dimensional and nonuniform. The system knew only
the maximum distance (4 cm) between the reference microphone (Mic. 1) and the others. For each setup, we selected
some of the four speakers (a120, b120, c120, c170) as
dominant target sources, and the others were kept silent. The

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TABLE IAVERAGE SIR IMPROVEMENT FOR SINGLE-TARGET CASES (DECIBELS)TABLE IIAVERAGE SDR FOR SINGLE-TARGET CASES (DECIBELS)

Fig. 7. Experimental conditions.

six speakers away from the microphones were used to provide
interference sources for every setup. The frame size of STFT
0.015 to specify the(4) was 1024 (128 ms). We used
set of target sources by (23).
The performance was evaluated in terms of the signal-to-interference ratio (SIR) improvement for each output . The im. These
provement was calculated by
two types of SIR’s are defined by
dB

dB

where
is defined in (3), andis the component ofthat appears at output:.
The performance was also evaluated by signal-to-distortion
ratio (SDR) to allow us to observe the unwanted effects of timefrequency masking, such as musical noises. SDR is defined by
dB
where
and
are scalars for aligning delay and amplitude. They are specified so that the power of the denominatoris minimized.
B. Single-Target Cases.
First we show the results for single-target cases
Experiments were conducted with 16 combinations of sevenspeeches (one target six background interferences) for eachtarget position. The computational time was around 12 s for 6-s

speech mixtures. The program was coded in Matlab and run on
Athlon 64 FX-53 (2.4-GHz CPU clock).
Table I shows the average SIR improvements obtained onlywith ICA, and by the combination of ICA and time-frequency(T-F) masking. The SIR improvements clearly depend on theposition of the target source. Positions a120 and b120 werefairly good for enhancement. This is because the interferencescame from different directions. If we consider the speaker arrangement two-dimensionally, positions c120 and c170 seems
to be a hard position as many interferences came from similar directions. However, the result for position c170 was very good.
This is because the height of c170 was different from thoseof interferences, and the three-dimensionally arranged microphones enable the system to exploit this height difference.
We used three sets of parameters for function (30) specifying
a mask for each time-frequency slot. The shapes of these funcretions are shown in Fig. 6. Table I shows that a smaller
sulted in greater SIR improvements by T-F masking. However,were unnatural. Table II showssome sounds with a small
provided worse SDRs. Therefore, there isthat a smallerclearly a tradeoff between SIR improvement and SDR. We observed by informal listening tests that in many cases parameterproduced natural sounds with sufficientinterference suppression. Some sound examples can be foundon our web site.1
Fig. 8 shows example spectrograms. We can see that the target
speech was enhanced to a certain degree only with ICA. With
the combination of ICA and T-F masking, some residuals were
eliminated and the harmonic structure appeared more clearly inthe spectrogram (e.g., at time frames from 80 to 90). Moreover,
the active and inactive time of the target speech became clearer
with T-F masking.
Fig. 9 shows examples of envelopes and masks at 969 Hz.
Although the ICA output was close to the target component
at many time frames, some interference components were contained in the ICA output at some time frames (e.g., from 15 to20). This shows the limitation of ICA as a spatial filter, namelythat the number of interferences that can be eliminated is less1[Online].
dominant.

Available:

http://www.kecl.ntt.co.jp/icl/signal/sawada/demo/

SAWADA et al.: BLIND EXTRACTION OF DOMINANT TARGET SOURCES

2171

Fig. 10. Single-target clustering result for normalized basis vectors. Each point cshows the squared distance a
between a normalized basis vector and=C
its corresponding centroid. The four clusters are sorted according to
as shown in (23).

k 0 k

J j j

TABLE IIIAVERAGE SIR IMPROVEMENT FOR MULTITARGET CASES (DECIBELS)

Fig. 8. Spectrograms for 1) a mixture x (t), 2) an output signal y (t) obtained
only with ICA, 3) an output signal y (t) obtained by a combination of ICA and
T-F masking, and 4) the target source component x (t).

Fig. 11. Multitarget clustering result for normalized basis vectors (three dom cinant sources). Each point shows the squared distance a
between a
normalized basis vector and its corresponding centroid. The four clusters are= C as shown in (23).
sorted according to

k 0 k

J j j

this case. From this fact, the system decided that there was one
dominant target source. This clustering results were also used
to solve the permutation ambiguity of ICA based on (25).
C. Multitarget Cases

Fig. 9. Envelopes for a target signal, the total sum of all interferences, and the
ICA output corresponding to the target signal (above), and masks calculated foreach time frame (below).

than the number of sensors. The lower plot shows masks specified for each time frame. These masks show the activity of thetarget source at this frequency. By using these masks, the systemeliminated the interference components at the time frames from15 to 20, and improved the SIR at the output.
Fig. 10 shows an example clustering result for normalized
4 clusters, there was only one (thebasis vectors. Of the
0.015 in
leftmost one) that had a smaller variance than.
Next, we show the results for multitarget cases
Although we have performed experiments under various conditions, here we simply show the results for cases where threesources positioned at a120, b120, and c170 were dominant
3 . Experiments were conducted with ten combinationsof nine speeches (three targets
six background interferences). The computational time was around 15 s for 6-s speech
mixtures.
Table III shows the average SIR improvements obtained
solely with ICA, and by a combination of ICA and T-F
masking. Even with such hard input SIRs, the system succeeded in enhancing the target sources to a certain degree.
Fig. 11 shows an example clustering result for normalizedbasis vectors. In this multitarget case, there were three clusters
0.015, and the systemthat had smaller variances thandecided that there were three dominant target sources. Again,

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these clustering results were also used to solve the permutation
ambiguity of ICA.
VII. CONCLUSION
We have proposed a new method for extracting dominanttarget sources and suppressing other interference sources
blindly. The method is based on a two-stage process where ICA
is first applied to mixtures and then time-frequency masking isused to reduce residuals, which are caused by the limitation of. The main contribution of this paper is to
ICA whenpropose the following two new techniques.
1) Basis vector normalization and clustering, which decidesof target sources and aligns the permutathe number
tion ambiguity of ICA. The new method does not need toknow the array geometry and therefore makes it easy to
use a three-dimensional nonuniform arrangement of sensors without exact measurement or calibration.
2) Specifying time-frequency masks from the angle
between the basis vector of a target source and a sample
vector in a spatially whitened space. The angle indicates
whether or not the corresponding target source is active at.
a time-frequency slot
produced by
Both techniques manipulate basis vectors
ICA, which is a statistical tool for blind processing.
We obtained good experimental results for extracting dominant sources out from many interference sources mixed in a real
reverberant room. The experiments shown in this paper used impulse responses to evaluate extraction performances in terms of
SIR and SDR. We also have tested the system in a live situation where loudspeakers and/or human speakers made speechsounds in a real room. The results were as good as the simulated ones where the impulse responses were used.
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Hiroshi Sawada (M’02–SM’04) received the B.E.,
M.E., and Ph.D. degrees in information science from
Kyoto University, Kyoto, Japan, in 1991, 1993, and2001, respectively.
In 1993, he joined the NTT Communication Science Laboratories, Kyoto. From 1993 to 2000, he wasengaged in research on the computer-aided design ofdigital systems, logic synthesis, and computer architecture. In 2000, he stayed at the Computation Structures Group of MIT for six months. Since 2001, hehas been teaching a class on computer architecture
at Doshisha University, Kyoto. Since 2000, he has been engaged in researchon signal processing, microphone array, and blind source separation (BSS).
More specifically, he is working on the frequency-domain BSS for acoustic convolutive mixtures using independent component analysis (ICA). He is now a
Senior Research Scientist at NTT Communication Science Laboratories. He is
the author or coauthor of two book chapters, 19 journal articles, and more than
70 conference papers.

SAWADA et al.: BLIND EXTRACTION OF DOMINANT TARGET SOURCES

Dr. Sawada received the Ninth TELECOM System Technology Award forStudent from the Telecommunications Advancement Foundation in 1994, andthe Best Paper Award of the IEEE Circuit and System Society in 2000. He isa member of the IEICE and the ASJ. He served as an organizing committee
member for ICA 2003 in Nara and the communications chair for IWAENC 2003
in Kyoto. He is an international program committee member for ICA 2006.

Shoko Araki (M’01) received the B.E. and the 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, Kyoto, Japan. Her research interests
include array signal processing, blind source separation applied to speech signals, and auditory scene
analysis.
Ms. Araki received the TELECOM System
Technology Award from the Telecommunications
Advancement Foundation in 2004, the Best Paper Award of the IWAENC in
2003, and the 19th Awaya Prize from the Acoustical Society of Japan (ASJ) in
2001. She is a member the IEICE and the ASJ.

Ryo Mukai (A’95–M’01–SM’04) received the B.S.
and the M.S. degrees in information science from the
University of Tokyo, 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, Kyoto, Japan, where he is engaged in researchof blind source separation. His current researchinterests include digital signal processing and its applications.

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Mr. Mukai received the Sato Paper Award from the Acoustical Society ofJapan (ASJ) in 2005 and the Paper Award from the IEICE in 2005. He is amember of the ACM, ASJ, IEICE, and IPSJ.

Shoji Makino (A’89–M’90–SM’99–F’04) received
the B.E., M.E., and Ph.D. degrees from Tohoku University, Sendai, Japan, in 1979, 1981, and 1993, respectively.
He joined NTT in 1981. He is now an Executive
Manager at the NTT Communication Science Laboratories, Kyoto, Japan. He is also a Guest Professor
at the Hokkaido University, Sapporo, Japan. His
research interests include adaptive filtering technologies and realization of acoustic echo cancellation,
blind source separation of convolutive mixtures ofspeech. He is the author or coauthor of more than 200 articles in journals andconference proceedings and has been responsible for more than 150 patents.
Dr. Makino received the TELECOM System Technology Award from the
TAF in 2004, the Best Paper Award from the IWAENC in 2003, the Paper Award
from the IEICE in 2005 and 2002, the Paper Award of the ASJ in 2005 and 2002,
the Achievement Award from the IEICE in 1997, and the Outstanding Technological Development Award from the Acoustical Society of Japan (ASJ) in 1995.
He is a member of the Awards Board and a member of the Conference Boardof the IEEE SP Society. He is an Associate Editor of the IEEE TRANSACTIONSON SPEECH AND AUDIO PROCESSING and an Associate Editor of the EURASIP
Journal on Applied Signal Processing. He is a member of the Technical Committee on Audio and Electroacoustics of the IEEE SP Society as well as theTechnical Committee on Blind Signal Processing of the IEEE Circuits and Systems Society. He is also a member of the International ICA Steering Committee
and the Organizing Chair of the ICA2003 in Nara. He is the General Chair of
the WASPAA2007 in Mohonk, NY, and the General Chair of the IWAENC2003
in Kyoto. He is the Chair of the Technical Committee on Engineering Acousticsof the IEICE and the ASJ. He is a council member of the ASJ and a member ofthe EURASIP and the IEICE.