Hiroshi Saruwatari

Blind source separation combining independent component analysis and beamforming

We describe a new method of blind source separation (BSS) on a microphone array combining subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband ICA-based BSS section with estimation of the direction of arrival (DOA) of the sound source, (2) null beamforming section based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity. Using this technique, we can resolve the low-convergence problem through optimization in ICA. To evaluate its effectiveness, signal-separation and speech-recognition experiments are performed under various reverberant conditions. The results of the signal-separation experiments reveal that the noise reduction rate (NRR) of about 18 dB is obtained under the nonreverberant condition, and NRRs of 8 dB and 6 dB are obtained in the case that the reverberation times are 150 milliseconds and 300 milliseconds. These performances are superior to those of both simple ICA-based BSS and simple beamforming method. Also, from the speech-recognition experiments, it is evident that the performance of the proposed method in terms of the word recognition rates is superior to those of the conventional ICA-based BSS method under all reverberant conditions.

Blind separation of acoustic signals combining SIMO-model-based independent component analysis and binary masking

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.

Estimation of Shape Parameter of GGD Function by Negentropy Matching

In this paper we present a novel method for the estimation of the shape parameter of the Generalized Gaussian Distribution (GGD) function for the leptokurtic and Gaussian signals by matching negentropy of GGD function and that of data approximated by some non-polynomial functions. The negentropy of GGD function is monotonic function of its shape parameter for values corresponding to super-Gaussian and Gaussian distribution family. The simulation results have been compared with those obtained by existing methods such as Mallat’s method and Kurtosis matching method. It has been found that the proposed method is effective and useful in the cases where we have a few observation samples and distribution is highly spiky.

Blind Source Separation Combining Simo-Ica and Simo-Model-Based Binary Masking

A new two-stage blind source separation (BSS) for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based ICA and a new SIMO-model-based binary mask processing are combined. SIMO-model-based ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to the attractive property, novel SIMO-model-based binary mask processing 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 using the proposed method compared with the conventional BSS methods

High-Presence Hearing-Aid System using DSP-Based Real-Time Blind Source Separation Module

Real-time two-stage blind source separation (BSS) method for convolutive mixtures of speech is now being studied by the authors, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a SIMO-model-based binary masking are combined. In addition, we have developed a pocket-size real-time DSP module implementing the two-stage BSS method. In this paper, we introduce a high-presence hearing-aid system which can reduce the interference sound and reproduce the target sound while keeping the directivity, and realize the system with the real-time BSS module. To evaluate it, we carried out the objective and subjective experiments using 9 users. From these results, it is revealed that the decomposition performance and the directivity maintenance of the proposed system are superior to those of conventional methods.

Blind Separation and Deconvolution for Convolutive Mixture of Speech Combining SIMO-Model-Based ICA and Multichannel Inverse Filtering

We propose a new two-stage blind separation and deconvolution strategy for multiple-input multiple-output (MIMO)-FIR systems driven by colored sound sources, in which single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. After the separation by the SIMO-ICA, a blind deconvolution technique for the SIMO model can be applied even when each source signal is temporally correlated and the mixing system has a nonminimum phase property. The simulation results reveal that the proposed algorithm can successfully achieve separation and deconvolution of a convolutive mixture of speech, and outperforms a number of conventional ICA-based BSD methods.

A Self-Generator Method for Initial Filters of SIMO-ICA Applied to Blind Separation of Binaural Sound Mixtures

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