Blind Speech Separation (Signals and Communication by Shoji Makino, Te-Won Lee, Hiroshi Sawada

By Shoji Makino, Te-Won Lee, Hiroshi Sawada

This is often the world’s first edited publication on self reliant part research (ICA)-based blind resource separation (BSS) of convolutive combos of speech. This publication brings jointly a small variety of prime researchers to supply tutorial-like and in-depth therapy on significant ICA-based BSS issues, with the target of changing into the definitive resource for present, accomplished, authoritative, and but available remedy.

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Extra resources for Blind Speech Separation (Signals and Communication Technology)

Example text

In other words, a particular source appears to be coming from different directions to the beamformer. In order to avoid spatial-aliasing, the sensor spacing should be selected such that d ≤ λ/2, where λ is the wavelength of the sound signal. Higher signal frequencies need smaller sensor spacing, and lower signal frequencies need a larger spacing between the sensors to avoid aliasing. Another significant problem is the computational complexity of solving the permutation as the number of sources and sensors increases.

Spectral correlation based permutation removal. To deal with more complicated mixing conditions, a method based on spectral correlation was first proposed in [36]. This method is based on the following two properties of speech signals: 1. Speech signals are inherently nonstationary; however, for short-enough time segments, speech signal can be considered to be stationary. 2. Nonstationarity in speech arises mainly due to amplitude modulation. The first property allows the use of the DFT for transforming the mixture data in the spectral domain, whereas the second property allows for the 1 Convolutive Blind Source Separation B(w) 31 P(w) output input Separation System Permutation Resolver Frequency Domain Separation System Fig.

Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. Wiley Series on Adaptive and Learning Systems, 2001. 29. A. Hyvarinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, Oct. 1997. 30. P. P. Vaidyanathan, Multirate Systems and Filter Banks. Englewood Cliffs, NJ: Prentice-Hall, 1992. 31. S. C. Douglas, “An adaptive constraint method for paraunitary filter banks with applications to spatio-temporal subspace tracking,” Accepted for publication in EURASIP J.

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