A self-generator method for initial filters of SIMO-ICA applied to blind separation of binaural sound mixtures

Conference Paper · November 2005with2 Reads
DOI: 10.1109/ASPAA.2005.1540156 · Source: IEEE Xplore
Conference: Applications of Signal Processing to Audio and Acoustics, 2005. IEEE Workshop on
Blind separation of binaural mixed sounds using single-input multiple-output (SIMO)-model-based independent component analysis (SIMO-ICA) with self-generator for initial filter (SIMO-ICA-SG) is now being studied by the authors. This method contains frequency-domain ICA (FDICA-PB), single-talk detection, direction of arrival (DOA) estimation, head related transfer function (HRTF) matrix bank, and SIMO-ICA. This paper describes robustness of SIMO-ICA-SG against the mismatch of HRTF matrix bank. To evaluate it, the sound decomposition experiments are carried out under the real acoustic conditions. The experimental results reveal that the decomposition performance of the proposed method with mismatched HRTF matrix bank is superior to those of the conventional methods, and almost the same as those of the proposed method with matched one
    • "SIMO-ICA has the advantage that the separated signals still maintain the spatial qualities of each sound source, in comparison with conventional ICA-based BSS methods. Clearly, this attractive feature makes SIMO-ICA highly applicable to high-fidelity acoustic signal processing, for example, binaural sound separation [21]. "
    [Show abstract] [Hide abstract] ABSTRACT: A new two-stage blind source separation (BSS) method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, our novel SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by the proposed method compared with that achieved by conventional BSS methods. In addition, the real-time implementation of the proposed BSS is illustrated.
    Full-text · Article · Jan 2006
  • [Show abstract] [Hide abstract] ABSTRACT: http://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=83954 博士 (Doctor) 工学 (Engineering) 博第457号 甲第457号
    Article · · EURASIP journal on advances in signal processing
  • [Show abstract] [Hide abstract] ABSTRACT: Blind source separation for convolutive mixture of speech signals has been addressed in many literatures. However, widely applied Multichannel Blind Deconvolution (MBD) method suffers whitening effect or arbitrary filtering problem which results in dramatic decrease of Automatic Speech Recognition system's performance. In present paper, a new MBD based multistage method is proposed, in which contributions of each source to every microphone are final goal rather than original signals. In detail, MBD is first implemented using entropy maximization criterion combined with Natural Gradient (NG) algorithm, then compensation matrix is constructed, based on which sources are recovered to its contribution to every microphone, i.e., whitening effect or arbitrary filtering problem has been transformed to fixed filtering problem. After compensation processing, for a certain source, it becomes Single Input and Multi-Output (SIMO) problem. Thus, not only spatial quality of source can be preserved, but also SIMO blind deconvolution can be further applied to fully recover temporal structure of speech signal. Finally, experiment shows validity and superiority over other methods in both spectra preservation efficiency and speed.
    Conference Paper · Jan 2006 · EURASIP journal on advances in signal processing