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PAPER

Special Section on Multi-channel Acoustic Signal Processing

Underdetermined Blind Separation of Convolutive Mixtures of
Speech Using Time-Frequency Mask and Mixing Matrix Estimation
Audrey BLIN†∗a) , Shoko ARAKI† , Nonmembers, and Shoji MAKINO† , Member

SUMMARY
This paper focuses on the underdetermined blind source
separation (BSS) of three speech signals mixed in a real environment frommeasurements provided by two sensors. To date, solutions to the underdetermined BSS problem have mainly been based on the assumption that the
speech signals are suﬃciently sparse. They involve designing binary masksthat extract signals at time-frequency points where only one signal was assumed to exist. The major issue encountered in previous work relates tothe occurrence of distortion, which aﬀects a separated signal with loud musical noise. To overcome this problem, we propose combining sparsenesswith the use of an estimated mixing matrix. First, we use a geometricalapproach to detect when only one source is active and to perform a preliminary separation with a time-frequency mask. This information is then used
to estimate the mixing matrix, which allows us to improve our separation.
Experimental results show that this combination of time-frequency mask
and mixing matrix estimation provides separated signals of better quality(less distortion, less musical noise) than those extracted without using theestimated mixing matrix in reverberant conditions where the reverberant
time (TR) was 130 ms and 200 ms. Furthermore, informal listening tests
clearly show that musical noise is deeply lowered by the proposed method
comparatively to the classical approaches.
key words: source separation, blind, underdetermined, convolutive,
sparseness of speech, distortion, time-frequency mask, mixing matrix, musical noise

1.

Introduction

The title of this paper includes the words blind, underdetermined and convolutive and each should be paid a particularattention. They are key words with regard to our work andeach of them reflects a level of diﬃculty we have to face.
Therefore, in this introduction, we will focus on each in turnand explain the way they relate to each other.
First, the adjective blind stresses the fact that the source
signals are not observed and that no information is available
about how the sources are mixed (e.g., [1]). Blind source
separation (BSS) actually refers to the problem of recovering signals from several observed linear mixtures. The
weakness of the prior information is precisely the strengthof the BSS model.
In this paper, we concentrate on the audio BSS issue,
which is currently a major research field. Probably the bestknown problem in auditory scene analysis is that of theManuscript received November 2, 2004.
Manuscript revised January 21, 2005.
Final manuscript received March 11, 2005.
†
The authors are with the NTT Communication Science Laboratories, NTT Corporation, Kyoto-fu, 619-0237 Japan.
∗
Presently, with the Université du Québec, INRS EMT, 800
de la Gauchetière Ouest, Suite 6900, Montréal, Québec, H5A1K6,
Canada.
a) E-mail: audrey blin@hotmail.com
DOI: 10.1093/ietfec/e88–a.7.1693

cocktail party.
Recently, many methods have been proposed to solve
the BSS problem of audio signals in real environments. A
common approach involves using independent component
analysis (ICA) (e.g., [2], [3]). However, most of these methods consider a determined problem, where we have as manysources as sensors (e.g. [4]–[6]).
Here, in an attempt to deal with a more contemporary
issue, we have decided to concentrate on an underdetermined case where there are more sources than sensors. This
choice comes from the fact that, in a real situation, most of
the time, there are ambient and uncountable noise sources.
Furthermore, manufacturers do not want many sensors fortheir small and inexpensive products. Therefore, for real applications, we have to consider the underdetermined case.
Moreover, to be more realistic, we should consider the
mixing process of the speech signal to be convolutive because most speech signals are recorded with their reverberation. Below we focus on the two remaining key words andlook at the kinds of solutions that have already been proposed for dealing with the issues that the words refer to.
The true obstacle raised by underdetermination relates
to the fact that a simple inversion of the mixing matrix will
not lead us to a solution in as much as the mixing system is
not square and therefore not invertible. Furthermore, even if
we knew the mixing matrix exactly, we would not be able torecover the original signals because part of the information
is lost during the mixing process.
Thus far, solving the BSS problem in an underdetermined case has mainly consisted in assuming that the speech
signals were suﬃciently sparse [7]–[11], which legitimizes
the extraction of each source.
It is our understanding that there are two approaches
relying on sparseness that will solve the underdetermined
BSS. The first involves the clustering of time-frequency
points with binary masks [10]. It emerges that, if the signals are suﬃciently sparse, namely if most of the samples
of a signal are almost zero, we can assume that the sources
rarely overlap. [10] uses this assumption and extracts eachsignal using a time-frequency binary mask. The second approach is based on ML estimation, where the sources are estimated by l1 -norm minimization after an evaluation of themixing matrix [7], [9], [12], [13].
Although, dealing with the underdetermined BSS is already very tough, it is still possible to present ourselves witha greater challenge by considering the underdetermined convolutive BSS issue, a solution to which has already beenc 2005 The Institute of Electronics, Information and Communication Engineers
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sought [10]. However, this approach leads to the discontinuous zero-padding of separated signals because all the unknown samples (e.g. when several sources are active at thesame time) are set at zero by a binary mask. Consequently
such separated signals are greatly distorted, and therefore aloud musical noise is heard. In contrast to the ML approach,which has excited great interest in recent years, only a few
trials have been undertaken related to the convolutive case.
Of course, convolutive mixtures can be seen frequency bin
by frequency bin as instantaneous mixtures but, as such a
conversion results in complex-valued signals, the l1 -norm
minimization is not applicable straightforwardly and would
need to be expanded or alternated. Consequently, due to ahigh level of complexity, the ML approach is rather avoided
in the convolutive case.
The objective of our work is to provide a satisfactory
solution to the underdetermined convolutive BSS by ensuring that any musical noise is minimized. In this paper, we
focus on the underdetermined BSS of three speech signals
mixed in a real environment from measurements provided
by two sensors.
It should be remembered that one of the ways to overcome the BSS issue in a determined problem is to estimate
and then invert the mixing matrix modelling of our system[8]. Recently, [14] proposed an approach for estimating
the separating matrix in the instantaneous determined BSS
problem. However, here, where sources outnumber sensors, the mixing matrix and separating matrix are no longer
square and such solutions cannot be used. Nevertheless, thisgave us the idea of combining the sparseness properties ofspeech signals with an estimation of the mixing matrix.
Our suggestion for eliminating the distortion problem
in convolutive underdetermined BSS is to combine sparseness with a mixing matrix estimation [15]. First, using thesparseness, we detect the time points when only one sourceis active. This information is then used to estimate the mixing matrix. Subsequently, by using sparseness investigations, we were able to work under conditions where one of
the sources could be omitted by a time-frequency mask. The
separation of the residual signals using the inverse of the estimated mixing matrix then follows. By this one source removal, the zero-padding of the separated signal is expected
to be less corrupting than with the previous binary mask approach. Results and informal listening show that we indeed
obtain more information about the signals to be separated
and significantly lowered the musical noise comparatively
to the classical approaches. As a consequence, we can reduce the zero-padding eﬀect from which the musical noise
originates. A complementary approach, also relying on one
source removal, has been proposed by the same authors [16],where they use the ICA algorithm to the residual signals.
Apart from Araki’s approach and so as to overcome the previously mentioned lack of quality, here, we attempt to utilize
an estimated mixing matrix to separate the residual.
The organization of this paper is as follows. Section 2
describes our problem statements and essential investigations with regard to sparseness are described in Sect. 3. Sec-

Fig. 1 An example in the frequency domain (250 Hz) of a probability
density function of a speech signal.

tion 4 is a step-by-step presentation of the proposed method,whose results are provided in Sect. 5. Section 6 provides ourconclusions.
2.

Problem Statements and Notations

In this paper, we consider speech mixtures observed in areal room. In this case, as speeches are mixed with theirreverberation, the observed vectors x j ( j = 1, · · · , M) canbe modelled as convolutive mixtures of the source signals si(i = 1, · · · , N) as follows:
x j (n) =

N 

i=1

h ji (l)si (n − l + 1)(1)lwhere h ji is the impulse response from a source i to a sensor
j. As it is our first trial, we decided to deal with the simplest
case, i.e. where N = 3 sources and M = 2 sensors. Moreover, we assume that the source signals are sparse: namely
signals have large values at rare sampling points as shownby the probability density function (pdf) of a speech signal
in Fig. 1.
We use the Short Time Fourier Transform (STFT) to
convert our problem into a linear instantaneous mixture
problem as well as to improve the sparseness of the speech
signals [9] as shown in Fig. 2. In the time-frequency domain,
our system becomes:X( f, m) = H( f )S( f, m)(2)

where f is the frequency, m the frame index, H( f ) the 2 × 3
mixing matrix whose ( j, i) component is a transfer function
from a source i to a sensor j, X( f, m) = [X1 ( f, m), X2 ( f, m)]Tand S( f, m) = [S 1 ( f, m), S 2 ( f, m), S 3 ( f, m)]T , namely the
Fourier transformed observed signals and source signals, respectively. Our aim is to estimate three speech signals frommeasurements provided by two sensors.
3.

Sparseness Inquiries

Sparseness becomes greater as the number of zero samples contained in a source increases, which means that thesources overlap at infrequent intervals. Interesting investigations of the sparseness property of speech signals have

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1695(a) Time representation.

Fig. 3 Histogram of the number of active sources: 0, 1, 2 or 3 for a malemale-female combination recorded with a reverberation of 200 ms and fora DFTsize of 512.

(b) Frequency representation ( f =1078 Hz).
Fig. 2 Scatter plot of X2 versus X1 of three mixed speech for a malemale-female combination.

been proposed in [10] for anechoic speech signals and in[17] for two speech signals. Here, we give detailed investigations for three speech signals not only for anechoic signals
but also echoic signals.
Figure 3 is a histogram showing the number of sourcesthat are simultaneously active. In the figure, we consider thesignal si (n) is active when the signal si (n) has a greater amplitude than max(|sk (n)|)/10 (k = 1, 2, 3, n = wholelength).
It can be seen that there are many time points at which nosources are active and few where three sources are active.
We can infer from these observations that the signals aresparse and that three signals rarely overlap.
3.1 Measure of Overlapping
To determine the best representation, we investigated thesparseness more closely and checked the degree of signal overlap by utilizing a criterion called Approximate
W-Disjoint Orthogonality (WDO) defined by Yilmaz and
Rickard [10]. We use a mask:|S (f,m)| 1, if 20 log |Y jj(f,m)| > x(3)
Φ( j,x) ( f, m) =  0, otherwise

Fig. 4 Approximate WDO against the threshold x for a DFTsize of 512,a male-male-female combination using a reverberant time of 130 ms or
200 ms, which can be compared to the original recording.

where Y j ( f, m) is the DFT ofy j (n) =Nsi (n)(4)

i=1,i ji.e. y j (n) is the summation of the sources interfering withsource j. The approximate WDO is defined as:Φ( j,x) ( f, m)S j (f, m)2,
(5)
S j (f, m)2
 
where  f (x, y)2 = y x | f (x, y)|2 , where f is a function ofx and y.
This measures the percentage r j of source j energy fortime-frequency points where this source dominates the othersignals by r j % at x dB. From this criterion it emerges that, if
we can predict the time-frequency points at which a source
dominates the others by r j % at x dB, we should be able
to recover r j % of the energy of the original sources. If
r j is suﬃciently large, we can separate signals with little
distortion and vice-versa. For example in Fig. 4, if we want a
signal-to-interference ratio of 20 dB, only around 60% of ther j (x) = 100

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Fig. 5 Approximate WDO of anechoic measured data against the DFTsize for a threshold x of 10 dB and a sampling rate of 8 kHz, TR = 200 ms.

original power is recoverable, which means that almost half
the points are zero-padded by a mask and such distortion
cannot be avoided. Moreover Fig. 4 shows that reverberant
data have a lower Approximate WDO than anechoic data.
Hence separating reverberant data becomes more diﬃcult.
3.2 Sparsest Representation with STFT
To make the separation easier, we have to represent our datain the space domain where their degree of sparseness is thebest. Figure 5 shows the Approximate WDO against the different frame size for STFT, which called DFTsize (Discrete
Fourier Transform frame size) in this paper. From Fig. 5,
we can estimate the appropriate DFTsize for the easiest separation. Hence, from now, we will use a DFTsize of 512
for a 8 kHz sampling rate in as much as this DFTsize allows
the best recovery in terms of energy and therefore separated
signals are less distorted.
4.

Proposed Method

With previously reported methods [7], [9], [11], a major
drawbacks of the classical approaches was the occurrenceof distortion, i.e., musical noise. To overcome this problem,
we propose a three-step method. First, using the sparsenessof speech signals, we adopt a geometrical approach to determine the time-frequency masks that extract the time points
m when only one source is active [1st step], then we estimate
the mixing matrix [2nd step] and finally we reconstruct thesignals when two sources are active [3rd step].
• [1st step]Geometrical approachThis first step consists of detecting the frame indicesm when only one of the three sources is active for each frequency bin f . Scatter-plots of the measurements, as shownin Fig. 6, comprise three main lines (if the sources are sparseenough). According to Vielva et al. [9], these lines represent
the directions defined by the column vectors of the mixing
matrix. In other words, they can be seen as a representationof each source existing alone. In between two given directions, we find the time-frequency points modelling our system when two sources (those linked to the above directions)are active simultaneously.

Fig. 6 Scatter-plots of the mixtures at a frequency of 312 Hz for a malemale-female combination, a reverberation of 130 ms and a DFTsize of 512.

By setting narrow areas each containing only one line,
such as areas 1, 2 and 3 in Fig. 6, we are able to determine
when only one source is active and at the same time we canreconstruct the signals for these time-frequency points. That
is to say that three time-frequency binary masks can be designed, they are assigned the value 1 in areas 1, 2 or 3 andthe value 0 otherwise. This is basically the method exploitedin previous work [10]. However, as expected when using
such a rough approach, the quality of the separated signals
is unsatisfactory. Since the rate of recoverable energy is verylow (as shown in Fig. 9), we cannot avoid an important zeropadding, which makes the signals insuﬃciently continuous.
As a result, we hear considerable distortion i.e., loud musical noise. To recover this lack of quality, we attempt to complete our separation relying on the knowledge of the mixing
matrix. Distinctly from Araki’s approach [16], we try to utilize the estimated mixing matrix to separate the residual.
• [2nd step] Estimation of mixing matrix
Deville recovers the mixing matrix by estimating a certain cross-correlation parameter ratio over time-frequency
zones where only one source exists [8]. This ratio was thenproved to be equal to H2i /H1i (i = 1, 2, 3). In contrast
to Deville, here we are working with an underdetermined
convolutive case, however his approach gave us the idea ofmodelling our system in the time-frequency domain by:11
1 H11 (f)S 1 (f, m)
X1 (f, m)= H21 H22 H23 H12 (f)S 2 (f, m)
(6)
X2 (f, m)
H11
H12
H13
H13 (f)S 3 (f, m)
Therefore, by using time points estimated in the firststep when only S i (i = 1, 2, 3) is active, we have:
X1 (f, m) = H1i (f)S i (f, m)(7)
X2 (f, m) = H2i (f)S i (f, m)
whose ratio X2 ( f, m)/X1 ( f, m) provides one of the components of the mixing matrix H2i ( f )/H1i ( f ). For a stable estimation of the mixing matrix coeﬃcients, we estimated theBLIN et al.: UNDERDETERMINED BSS USING TIME-FREQUENCY MASK AND MIXING MATRIX ESTIMATION

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expectation of the ratio X2 /X1 :H2i ( f )/H1i ( f ) = E[X2 ( f, m)/X1 ( f, m)](8)where E[.] is the expectation at time m when X1 ( f, m) andX2 ( f, m) are in area i.
• [3rd step] Reconstruction of time-frequency points
when two sources are active
At this stage, it should be noted that knowing the mixing matrix does not enable us to separate the signals whenthree sources are active. This is because the mixing matrix is
not square and does not have an inverse. Deville [8] has only
applied his method to a squared mixing matrix. Nevertheless, it is still possible to rebuild the time-frequency points
when two sources are active, providing that for each frequency bin, we know the frame indices for which this caseoccurs. Once more this information is provided by the geometrical approach employed in the first step. But this time,
instead of setting the limits very close to the observed directions, we are considering much wider areas so as to enclose
the points located between two given directions. That is to
say that we utilize wider time-frequency masks here. Indeed
let us suppose that, for an estimated ( f, m) detected during
the first step, S 1 ( f, m) is zero (area 23 in Fig. 6), in this area,
our system becomes:
X1 (f, m)=
X2 (f, m)

1

1

H22
H12

H23
H13

H12 (f)S 2 (f, m)
H13 (f)S 3 (f, m)

1

1

H22
H12

H23
H13

−1

X1 (f, m)
X2 (f, m)Experimental conditions.

where




1
H’ ji ( f ) =  H jiHkij = k,j  k.

(13)

Hand Hkiji could be estimated by E[X j (f)/Xk (f)] for an area i.
Then we define a wide area so that we can re-define thesquare part of H’( f ) as in (9), then separate the signals in
the wide area by inverting the square part of H’( f ) as we
did in (10).

(9)
5.

Now the mixing matrix is square and can thus be inverted, leading to H12 ( f )S 2 ( f, m) and H13 ( f )S 3 ( f, m):
H12 (f)S 2 (f, m)=
H13 (f)S 3 (f, m)

Fig. 7(10)

That is, the separated signals Zi ( f, m) are obtained asfollows:
 


−1 
1
1
Z2 ( f, m)
X1 (f, m)= H22 H23(11)
Z3 ( f, m)
X2 (f, m)
H12
H13
Moreover, in this area, because the signals H12 ( f )S 2 ( f, m) and H13 ( f )S 3 ( f, m) are not too greatly zero-padded
due to the wide mask, we expect that the distortion of the estimated H12 ( f )S 2 ( f, m) and H13 ( f )S 3 ( f, m) will not be verylarge.
We proceed in the same way when S 3 ( f, m) is zero andobtain the estimates of H11 ( f )S 1 ( f, m) and H12 ( f )S 2 ( f, m).
It should be noted that, in Fig. 3, we have already confirmedthat we do not often have three sources active simultaneously. We could also extend our method to a general casewhere there are N sources and M sensors. In such a case,
we would write (6) as: X1 (f, m)  Hk1 (f)S 1 (f, m) 




..
..
(12)

 = H’( f ) 

.
.




X M (f, m)
H kN (f)S N (f, m)Experiments

5.1 Experimental Conditions
The recordings were made in a room with reverberation(TR=130 ms) using a two-element array of directional microphones 4 cm apart. The speech signals, sampled at 8 kHz,came from three directions: 120◦ (male), 90◦ (male) and 50◦(female) and the distance between the sources and the sensors was L = 55 cm (see, Fig 7).
5.2 Stability of Estimated Mixing Matrix Coeﬃcients
To evaluate the eﬃciency of our method, we need to know
about the stability of the mixing matrix we estimated in the
2nd stage. In Fig. 8, we plot the amplitude and phase of
the three coeﬃcients H2i ( f )/H1i ( f ) (i = 1, 2, 3) in (8). It isseen that our estimation generally oﬀers a great stability inthe whole, except for the low frequencies, where the timedelay between the two microphones, which are positioned
very close to each other, is harder to calculate with accuracy.
However we can observe the constant amplitude and linear
phase of the coeﬃcients.
5.3 Mask Justification
Figure 9 justifies our decision to use wide masks. Indeed if
we use narrow masks (e.g., area 3 in Fig. 6) as in the previous method, the recoverable power is only around 60% with

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Table 1 SIR and SDR calculated in dB for diﬀerent approaches, DFTsize=512, TR=130 ms.“sparseness” means that performances are evaluatedusing the narrow masks; for “area12” the wide masks are used but not the
mixing matrix (e.g., in the area comprising speech signal 1 and 2); for“H−1
area12 ” our mixing matrix is used over area 12. Likewise “area23” and“H−1
area23 ” are defined in area 23 comprising speech signals 2 and 3.
sparsenessarea12
area23H−1
area12
H−1
area23

11.6

SIR2
9.9
2.3
4.2
3.1
3.3

SIR3
10.6

SDR1
8.4
10.5

−2.7
8.7
7.6

SDR2
10.3
12.0
13.1
12.2
12.5

SDR3
3.4
6.5
7.2

Table 2 SIR and SDR calculated in dB for diﬀerent approaches, DFTsize=512, TR=200 ms. “sparseness” means that performances are evaluated using the narrow masks; for “area12” the wide masks are used but not
the mixing matrix (e.g., in the area comprising speech signal 1 and 2); for“H−1
area12 ” our mixing matrix is used over area 12. Likewise “area23” and“H−1
area23 ” are defined in area 23 comprising speech signals 2 and 3.

(a) Amplitude.

sparsenessarea12
area23H−1
area12
H−1
area23(b) Phase.
Fig. 8 Representation of the matrix coeﬃcients, male H23 ( f )/H13 ( f ) male H22 ( f )/H12 ( f ) - female H21 ( f )/H11 ( f ) combination, DFT size=512,
TR=130 ms.

Fig. 9 Approximate WDO against the threshold,
TR=130 ms.

SIR1
15.3
0.5

DFTsize=512,

a threshold of 10 dB whereas if we utilize wider masks (e.g.,area 23 in Fig. 6), we can recover over 75% of this power.
Consequently the technique using wide areas makes it possible to reduce the distortion of the separated signals, which
was our aim.
5.4 SIR and SDR Calculations
To evaluate the separation performance of our method,
we have chosen to calculate the Signal-to-Interference Ra-

SIR1
8.6
0.6
4.7

SIR2
5.7
0.5
−0.3
−1.1
0.4

SIR3
11.7

SDR1
0.9
5.4

1.9
2.4
9.5

SDR2
3.4
5.9
7.6
5.5
7.6

SDR3
0.7
7.1
4.6

tio (SIR) as a measure of separation performance and the
Signal-to-Distortion Ratio (SDR) as a measure of soundquality:zis (n)2(14)
S IRi = 10 log  n i
2i j n zis j (n)2n xksi (n)S DRi = 10 log (15)
2n (xksi (n) − αzisi (n − φ))where the permutation is solved before calculating SIR and
SDR, i.e. zi (t) is the estimation of si (t). zis j is the outputof the whole separating system at zi when only s j is active
and sk (k  j) does not exist, that is, zis j (n) = ui j (n) ∗ s j (n)where * denotes the convolution product, ui j (n) the impulse
responsefrom source s j to separated signal zi (ui j (n) =Mw(n)∗ hl j (n) with wil the impulse response from al=1 ilsensor l to a separated signal i). And xks j is the observation obtained by microphone k when only s j exists, i.e.,
xks j (n) = hk j (n) ∗ s j (n). α is a constant that compensates forthe amplitude diﬀerence and φ is an angle that fits the phase
diﬀerence between input xksi and output zisi . To evaluate theprevious method (sparseness only method), we calculated
SIR and SDR using both microphones’ measurements, andadopted the better values.
5.5 Separation Results
Tables 1 and 2 shows the results we obtained from ourmeasurements for diﬀerent degrees of reverberation. By“sparseness” we imply that we are evaluating the performances of our speech signals when employing the narrowmasks. By “area12,” we mean that the performances areevaluated for the wide masks, e.g., in the area comprising

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speech signal 1 and 2 without using our mixing matrix. Finally, by “H−1
area12 ,” we mean that we apply our mixing matrix
to area 12. Likewise “area23” and “H−1
area23 ” are defined inarea 23 comprising speech signals 2 and 3. Actually, weare comparing the conventional method with our proposed
method in the diﬀerent areas exploited.
As can be seen, the use of our proposed method allows
us to obtain less distorted signals. In areas 12 and 23, theSDR values are high, however, the SIR values in these areas
are bad as they consist of the mixtures two signals, which are
not separated. We performed informal listening tests and it
is important to note that, for the first signal in Table 1, although the two SDR results are very close, much less musical noise is heard when separation is undertaken using ourapproach rather than when only sparseness is used. To illustrate this point, we propose some sound samples on our website [18].
6.

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[17] M. Aoki, M. Okamoto, S. Aoki, H. Matsui, T. Sakurai, and
Y. Kaneda, “Sound source segregation based on estimating incident
angle of each frequency component of input signals acquired bymultiple microphones,” Acoust. Sci. & Tech, vol.22, no.2, pp.149–
157, 2001.
[18] http://www.kecl.ntt.co.jp/icl/signal/araki/B3M.html

Conclusion

We proposed a separation method for use when there are
more speech signals than sensors by combining a sparsenessapproach and an estimation of the mixing matrix. The experimental results are very encouraging in terms of quality
and suggest that the combination of a time-frequency mask
and a mixing matrix estimation is an approach that deserves
serious investigation.

Audrey Blin
received the Diplômed’Ingénieur in electrical engineering in 2003
from the Ecole Nationale Supérieure en Electricité Informatique et Radiocommunication de
Bordeaux, France. In 2003, she did a 6 monthinternship at NTT Communication Science Laboratories. Currently, she is with the INRS-EMT,
University of Québec, Canada as a Master Student since Autumn 2003. Her research interests include blind source estimation, equalization and separation of numerics and audio sig-

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nals.

Shoko Araki
received the B.E. and the
M.E. degrees in mathematical engineering andinformation physics from the University of Tokyo, Japan, in 1998 and 2000, respectively. In
2000, she joined NTT Communication Science
Laboratories, Kyoto. Her research interests include array signal processing, blind source separation applied to speech signals, and auditory
scene analysis. She 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 Acoustical Society of Japan (ASJ) in 2001. She is a member of theIEEE and the ASJ.

IEICE TRANS. FUNDAMENTALS, VOL.E88–A, NO.7 JULY 2005

1700

Shoji Makino
received the B.E., M.E., andPh.D. degrees from Tohoku University, Japan, in
1979, 1981, and 1993, respectively. He joined
NTT in 1981. He is now an Executive Manager at the NTT Communication Science Laboratories. He is also a Guest Professor at the
Hokkaido University. His research interests include adaptive filtering technologies and realization of acoustic echo cancellation, blind source
separation of convolutive mixtures of speech.
He received the TELECOM System Technology
Award of the TAF in 2004, the Best Paper Award of the IWAENC in 2003,
the Paper Award of the IEICE in 2002, the Paper Award of the ASJ in 2005
and 2002, the Achievement Award of the IEICE in 1997, and the Outstanding Technological Development Award of the ASJ in 1995. He is the author
or co-author of more than 200 articles in journals and conference proceedings and has been responsible for more than 150 patents. He is a memberof the Conference Board of the IEEE SP Society and an Associate Editorof the IEEE Transactions on Speech and Audio Processing. He is also an
Associate Editor of the EURASIP Journal on Applied Signal Processing.
He is a member of the Technical Committee on Audio and Electroacousticsof the IEEE SP Society as well as the Technical 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 Chairof the ICA2003 in Nara. He is the General Chair of the IWAENC2003
in Kyoto. He was a Vice Chair of the Technical Committee on Engineering Acoustics of the IEICE and the ASJ. Dr. Makino is an IEEE Fellow, acouncil member of the ASJ, and a member of the EURASIP.