Blind Source Separation Based on Multistage

A hands-free speech recognition system and a hands-free telecommunication system are essential for realizing an intuitive, unconstrained, and stress-free human-machine interface. In real acoustic environments, however, the speech recognition performance and a speech recording performance significantly degraded because we cannot detect the user’s speech with a high signal-to-noise ratio (SNR) owing to the interference signals such as noise. In this thesis, we introduce blind source separation (BSS), which is an approach for estimating original source signals only from the information of the mixed signals observed in each input channel. Many BSS methods based on independent component analysis (ICA) have been proposed for the acoustic signal separation. However, the performances of these methods degrade seriously particularly under extreme reverberant conditions. The ICA-based BSS can be classified into two groups in terms of the processing domain, i.e., frequency-domain ICA (FDICA) and time-domain ICA (TDICA

タダン ガタ ドクリツ セイブン ブンセキ ニ モトズク ブラインド オンゲン ブンリ

https://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100045225&oldid=83954博士 (Doctor)工学 (Engineering)博第457号甲第457号博士（工学）奈良先端科学技術大学院大

Stable Learning Algorithm for Blind Separation of Temporally Correlated Acoustic Signals Combining Multistage ICA and Linear Prediction

We newly propose a stable algorithm for blind source separation (BSS) combining multistage ICA (MSICA) and linear prediction. The MSICA is the method previously proposed by the authors, in which frequency-domain ICA (FDICA) for a rough separation is followed by time-domain ICA (TDICA) to remove residual crosstalk. For temporally correlated signals, we must use TDICA with a nonholonomic constraint to avoid the decorrelation effect from the holonomic constraint. However, the stability cannot be guaranteed in the nonholonomic case. To solve the problem, the linear predictors estimated from the roughly separated signals by FDICA are inserted before the holonomic TDICA as a prewhitening processing, and the dewhitening is performed after TDICA. The stability of the proposed algorithm can be guaranteed by the holonomic constraint, and the pre/dewhitening processing prevents the decorrelation. The experiments in a reverberant room reveal that the algorithm results in higher stability and separation performance

Blind Source Separation of Acoustic Signals Based on Multistage Independent Component Analysis

Summer Meeting of Acoustical Society of Korea, August 2002.We propose a new algorithm for blind source separation (BSS), in which frequency-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, conventional TDICA fails to separate source signals under heavily reverberant conditions because of hte low convergence in the iterative learning of hte inverse of the mixing system. On the other hand, the separation performance of conventional FDICA also degrades significantly because the independence assumption of narrow-band signals collapses when the number of subbands increases. In the proposed method, the separated signals of FDICA are regarded as the input signals for TDICA, and we can remove the residual crosstalk components of FDICA by using TDICA. The experimental results obtained under the reverberant condition reveal that the separation performance of hte proposed method is superior to that of conventional ICA-based BSS methods

Stable Learning Algorithm for Low-Distortion Blind Separation of Real Speech Mixture Combining Multistage ICA AND Linear Prediction

NOLISP2003: ISCA tutorial and research workshop on non-linear speech processing, May 20-23, 2003, Le Croisic, France.We propose a stable algorithm for blind source separation (BSS) combining multistage ICA (MSICA) and linear prediction. The MSICA in which frequency-domain ICA (FDICA) for a rough separation is followed by time-domain ICA (TDICA) to remove residual crosstalk. For temporally correlated signals, we must use TDICA with a nonholonomic constraint to avoid the decorrelation effect from the holonomic constraint. However, the stability cannot be guaranteed in the nonholonomic case. To solve the problem, the linear predictors estimated from the roughly separated signals by FDICA are inserted before the holonomic TDICA as a prewhitening processing, and the dewhitening is performed after TDICA. The stability of the proposed algorithm, can be guaranteed by the holonomic constraint, and the pre/dewhitening processing prevents the decorrelation

Bund source separation based on Multi-Stage ICA combining frequency-domain ICA and time-domain ICA

ICASSP2002: IEEE International Conference on Acoustics, Speech and Signal Processing, May 13-17, 2002, Orlando, Florida, US.We propose a new algorithm for blind source separation (BSS), in which frequency-domain independent component analysis (FDICA) and time-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, the conventional TDICA fails to separate source signals under heavily reverberant conditions because of the low convergence in the iterative learning of the inverse of the mixing system. On the other hand, the separation performance of the conventional FDICA under reverberant conditions also degrades significantly because the independence assumption of narrowband signals collapses when the number of subbands increases. In the proposed method, the separated signals of FDICA are regarded as the input signals for TDICA, and we can remove the residual cross-talk components of FDICA by using TDICA. The experimental results under the reverberant condition reveal that the signal-separation performance of the proposed method is superior to that of the conventional ICA-based BSS methods

Fast-Convergence Blind Separation of More Than Two Sources Combining ICA and Beamforming

NSIP2005: International Workshop on Nonlinear Signal and Image Processing, May 18-20, 2005, Sapporo, Japan.We propose a new blind source separation (BSS) algorithm for multiple source signals. Independent component analysis (ICA) and beamforming are combined to resolve the slow-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (a) frequency-domain ICA with direction-of-arrival (DOA) estimation using a Lloyd clustering algorithm; (b) beamforming based on the estimated DOA; (c) integration of (a) and (b) based on the algorithm diversity in both iteration and frequency domain. The separation matrix obtained by ICA is temporally substituted by the matrix based on beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. Experimental results reveal that the source-separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions