New PDF release: Blind Speech Separation (Signals and Communication

By Shoji Makino, Te-Won Lee, Hiroshi Sawada

ISBN-10: 1402064780

ISBN-13: 9781402064784

ISBN-10: 1402064799

ISBN-13: 9781402064791

This is often the world’s first edited e-book on autonomous part research (ICA)-based blind resource separation (BSS) of convolutive combinations of speech. This e-book brings jointly a small variety of top 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, finished, authoritative, and but available therapy.

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

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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. 4. Frequency-domain blind source separation with permutation solver.

46. L. C. Parra and C. V. Alvino, “Geometric source separation: Merging convolutive source separation with geometric beamforming,” IEEE Trans. Speech Audio Processing, vol. 10, no. 6, pp. 352–362, Sept. 2002. 47. S. C. Douglas, H. Sawada, and S. Makino, “A spatio-temporal FastICA algorithm for separating convolutive mixtures,” IEEE Int. Conf. , Speech, Signal Processing, vol. 5, Mar. 2005, pp. 165–168. 48. S. C. Douglas, M. Gupta, H. Sawada, and S. Makino, “Spatio-temporal FastICA algorithms for the blind separation of convolutive mixtures,” IEEE Trans.

Audio Speech Language Processing, vol. 15, no. 5, pp. 1511–1520, July 2007. jp Abstract. This chapter explains the frequency-domain approach to the blind source separation of acoustic signals mixed in a real room environment. With the application of short-time Fourier transforms, convolutive mixtures in the time domain can be approximated as multiple instantaneous mixtures in the frequency domain. So, separation is performed in each frequency bin with a simple instantaneous separation matrix. We employ complex-valued independent component analysis (ICA) to calculate the separation matrix.

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


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