IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 15, NO. 5, JULY 20071509

Introduction to the Special Section on Blind Signal
Processing for Speech and Audio Applications

P

EOPLE can engage in comprehensible conversations ata noisy cocktail party. This is the well-known “cocktail-party effect,” whereby our ears can extract what a person
is saying under such conditions. The aim of blind signal
processing for speech and audio applications is to provide
computers with this ability, thus enabling them to determine
individual speech waveforms from mixtures.
Blind signal processing has previously been applied to avariety of problems including wireless communication andbiomedical applications. However, as speech and audio signal
mixtures in a natural (i.e., reverberant) environment are generally convolutive mixtures, they involve a structurally much
more challenging task than instantaneous mixtures, which areprevalent in many other applications. Here, we mainly focus
on the blind source separation (BSS) and blind dereverberationof speech and audio signals under reverberant conditions. BSS
is an approach for estimating source signals using only information about their mixtures observed in each input channel.
The estimation is performed without possessing information oneach source, such as its frequency characteristics and location,or on how the sources are mixed.
For overdetermined or critically determined BSS where thenumber of sources is smaller than or equal to the number of microphones, BSS using independent component analysis (ICA)
has gained a firm position. This approach utilizes spatial diversity to discriminate between desired and undesired components,
i.e., it reduces the undesired component by forming a spatial nulltowards them.
Underdetermined BSS, where there are fewer microphones
than source signals, is a challenging problem because of theinherently adverse conditions. ICA cannot be used for this case,but the sparseness of audio sources is very useful; we can utilize
time–frequency diversity, where sources are active in different
regions of the time–frequency plane. Inspiration for solving thisdifficult problem may also come from an understanding of themechanisms involved in human auditory scene analysis. Evenwith a single microphone, we can separate a mixture by using
the harmonicity and temporal structure of the sources. We canbuild a probabilistic framework by assuming a source model andseparate a mixture by maximizing the a posteriori probability ofthe sources given the observations.
The dereverberation of speech signals is also a challenging
problem. There are two approaches to blind dereverberation.

The first relies on the blind channel identification of acoustic
systems and calculates inverse filters to obtain the dereberverated signal. On the other hand, the second approach does not
explicitly try to estimate the channel or channel-inverse.
The aim of this special section is to offer an opportunity tolink these techniques in different areas and to find effective
ways of solving this problem. After the review process, we selected the following seven papers for publication that span theapproaches identified above. Overdetermined or critically determined BSS is represented by the spatio–temporal extensionsof the FastICA algorithm proposed by Douglas et al., a nonparametric independent vector analysis algorithm proposed by
Lee and Lee, and the blind adaptive beamforming study of Warsitz and Haeb-Umbach. Underdetermined BSS is tackled by exploiting spectral sparseness in the papers of Aissa-El-Bey et al.
and He et al., and a model-based approach with a single channel
is taken by Ozerov et al. Robust dereverberation of speech is addressed in the paper by Furuya et al.
We hope that this special issue will stimulate interest in thechallenging area of BSS for speech and audio applications, andwe look forward to seeing an increasing body of high-quality
research aligned to this idea.
We would like to express our gratitude to the authors of thepapers in this special section and also to the more than 70 reviewers who helped us evaluate the submissions. We also thankIsabel Trancoso, the Editor-in-Chief who initiated this project,and Mari Ostendorf, the Editor-in-Chief under whom it was concluded. Finally, thanks to Kathy Jackson for her administrative
assistance throughout the process.

Digital Object Identifier 10.1109/TASL.2007.899167

1558-7916/$25.00 © 2007 IEEE

SHOJI MAKINO, Guest Editor
NTT Communication Science Laboratories
Kyoto 619-0237, Japan
maki@cslab.kecl.ntt.co.jp
TE-WON LEE, Guest Editor
University of California at San Diego
La Jolla, CA 92093-0523 USA
tewon@ucsd.edu
GUY J. BROWN, Guest Editor
University of Sheffield
Sheffield S1 4DP, U.K.
g.brown@dcs.shef.ac.uk

1510IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 15, NO. 5, JULY 2007

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 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 and blindsource separation of convolutive mixtures of speech.
Dr. Makino received the ICA Unsupervised Learning Pioneer Award in 2006. He is the authoror coauthor of more than 200 articles in journals and conference proceedings and is responsible
for more than 150 patents. He is a Tutorial speaker at ICASSP2007. He is a member of both theAwards Board and the Conference Board of the IEEE Signal Processing Society. He was an Associate Editor of the IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING. He is a member ofthe Technical Committee on Audio and Electroacoustics of the IEEE Signal Processing Society andthe Chair-Elect of the Technical Committee on Blind Signal Processing of the IEEE Circuits andSystems Society. He is a member of the International IWAENC Standing committee and a member of the International ICA Steering
Committee. He is the General Chair of the WASPAA2007 at Mohonk, New Paltz, NY, the General Chair of the IWAENC2003 inKyoto, and the Organizing Chair of the ICA2003, Nara, Japan.

Te-Won Lee (S’96–M’03–SM’06) received the diploma degree and the Ph.D. degree (summa cumlaude) in electrical engineering from the University of Technology Berlin, Berlin, Germany, in1995 and 1997, respectively.
He is an Associate Research Professor at the Institute for Neural Computation, University ofCalifornia at San Diego, La Jolla, and a Collaborating Professor in the Biosystems Department,
Korea Advanced Institute of Science and Technology (KAIST), Daejeon. He is also Co-Founder,President, and Chief Technical Officer at SoftMax, Inc., a technology-driven company in San Diegothat focuses on commercializing applications of its signal separation technology He was a MaxPlanck Institute Fellow (1995–1997) and a Research Associate at the Salk Institute for BiologicalStudies (1997–1999).
Dr. Lee received the Erwin-Stephan Prize for excellent studies (1994) from the Universityof Technology Berlin, the Carl-Ramhauser prize (1998) for excellent dissertations from theDaimler–Chrysler Corporation and the ICA Unsupervised Learning Pioneer Award (2007).

Guy J. Brown received the B.Sc. degree in applied science from Sheffield Hallam University,Sheffield, U.K., in 1988, and the Ph.D. degree in computer science and the M.Ed. degree from theUniversity of Sheffield, Sheffield, U.K., in 1992 and 1997, respectively.
He has been a Visiting Research Scientist at LIMSI-CNRS, Paris, France, ATR, Kyoto, Japan,The Ohio State University, Columbus, and the Helsinki University of Technology, Espoo, Finland.
He is currently a Senior Lecturer in computer science with the University of Sheffield. He has
a long-established interest in computational auditory modeling and also has research interests in
automatic speech recognition and music technology. He has authored and coauthored more than
80 papers in books, journals, and conference proceedings.