Blind Speech Separation

1. Front Matter

   Pages I-XV

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2. Multiple Microphone Blind Speech Separation with ICA

   1. Front Matter

      Pages 1-1

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   2. Convolutive Blind Source Separation for Audio Signals

      • Scott C. Douglas, Malay Gupta

      Pages 3-45

   3. Frequency-Domain Blind Source Separation

      • Shoji Makino, Hiroshi Sawada, Shoko Araki

      Pages 47-78

   4. Blind Source Separation using Space–Time Independent Component Analysis

      • Mike Davies, Maria Jafari, Samer Abdallah, Emmanuel Vincent, Mark Plumbley

      Pages 79-99

   5. TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation

      • Herbert Buchner, Robert Aichner, Walter Kellermann

      Pages 101-147

   6. SIMO-Model-Based Blind Source Separation – Principle and its Applications

      • Hiroshi Saruwatari, Tomoya Takatani, Kiyohiro Shikano

      Pages 149-168

   7. Independent Vector Analysis for Convolutive Blind Speech Separation

      • Intae Lee, Taesu Kim, Te-Won Lee

      Pages 169-192

   8. Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation

      • Michael Zibulevsky

      Pages 193-214

3. Underdetermined Blind Speech Separation with Sparseness

   1. Front Matter

      Pages 215-215

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   2. The DUET Blind Source Separation Algorithm

      • Scott Rickard

      Pages 217-241

   3. K-means Based Underdetermined Blind Speech Separation

      • Shoko Araki, Hiroshi Sawada, Shoji Makino

      Pages 243-270

   4. Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and L1-Norm Minimization

      • Stefan Winter, Walter Kellermann, Hiroshi Sawada, Shoji Makino

      Pages 271-304

   5. Bayesian Audio Source Separation

      • Cédric Févotte

      Pages 305-335

4. Single Microphone Blind Speech Separation

   1. Front Matter

      Pages 337-337

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   2. Monaural Source Separation

      • Gil-Jin Jang, Te-Won Lee

      Pages 339-364

   3. Probabilistic Decompositions of Spectra for Sound Separation

      • Paris Smaragdis

      Pages 365-386

   4. Sparsification for Monaural Source Separation

      • Hiroki Asari, Rasmus K. Olsson, Barak A. Pearlmutter, Anthony M. Zador

      Pages 387-410

   5. Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods

      • Sepp Hochreiter, Michael C. Mozer

      Pages 411-428

5. Back Matter

   Pages 429-432

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We are surrounded by sounds. Such a noisy environment makes it di?cult to obtain desired speech and it is di?cult to converse comfortably there. This makes it important to be able to separate and extract a target speech signal from noisy observations for both man–machine and human–human communication. Blindsourceseparation(BSS)isanapproachforestimatingsourcesignals using only information about their mixtures observed in each input channel. The estimation is performed without possessing information on each source, such as its frequency characteristics and location, or on how the sources are mixed. The use of BSS in the development of comfortable acoustic com- nication channels between humans and machines is widely accepted. Some books have been published on BSS, independent component ana- sis (ICA), and related subjects. There, ICA-based BSS has been well studied in the statistics and information theory ?elds, for applications to a variety of disciplines including wireless communication and biomedicine. However, as speech and audio signal mixtures in a real reverberant environment are generally convolutive mixtures, they involve a structurally much more ch- lenging task than instantaneous mixtures, which are prevalent in many other applications.

• Independent Component Analysis
• Support Vector Machine
• adaptive Filter
• algorithm
• algorithms
• blind source separation
• clustering
• filtering
• filters
• optimization
• sparse component analysis
• system identification
• unsupervised learning

• NTT Corporation, Soraku-gun, Japan

  Shoji Makino, Hiroshi Sawada

• University of California, San Diego, La Jolla, USA

  Te-Won Lee

Dr. Shoji Makino is an IEEE Fellow, Associate Editor of the IEEE Transactions on Speech & Audio Processing, and Executive Manager NTT Communication Science Laboratories. Dr. Makino was also co-editor on the succesful 2005 Springer book: Benesty - Speech Enhancement.

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