Yoshimitsu Mori

Two-stage blind source separation based on ICA and binary masking for real-time robot audition system

We newly propose a real-time two-stage blind source separation (BSS) for binaural mixed signals observed at the ears of humanoid robot, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and 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, and this yields that binary mask processing can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results obtained with a human-like head reveal that the separation performance can be considerably improved by using the proposed method in comparison to the conventional ICA-based and binary-mask-based BSS methods.

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.

Efficient Blind Source Separation Combining Closed-Form Second-Order ICA and Nonclosed-Form Higher-Order ICA

In this paper, first, we propose a computational-cost efficient blind source separation combining closed-form 2nd-order independent component analysis (ICA) and nonclosed-form higher-order ICA. The closed-form solution of the 2nd-order ICA has been recently presented by one of the authors. This finding motivates us to combine the closed-form 2nd-order ICA and higher-order ICA, where the preceding closed-form ICA produces a good initial value and the following higher-order ICA updates the separation filters from the advantageous status. Secondly, we utilize the proposed architecture to address an essential question that which type of statistics is more beneficial to ICA among non-stationarity and non-Gaussianity. This can be conducted owing to the attractive property that the closed-form ICA can provide a good estimate of the theoretical upper limitation of the separation performance among 2nd-order ICAs without suffering from poor-convergence problems. Experimental results reveal that the non-Gaussianity-based ICA can outperform the non-stationarity-based ICA.

Double-Talk Free Spoken Dialogue Interface Combining Sound Field Control With Semi-Blind Source Separation

In this paper we introduce a new double-talk free spoken dialogue interface combining sound field control and a source separation technique based on independent component analysis (ICA). First, sound field control provides silent zones on the microphone elements and prevents the response sound from being observed. In the second step, we propose a novel semi-blind source separation algorithm to suppress the error caused by fluctuation of the room transfer function. By using a direct input of response sound signal to ICA, a source separation problem can be converted to a supervised learning problem. Since the problem becomes easier, the proposed method showed higher performances than the method using blind source separation

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.

Binaural hearing-aid system using SIMO-model-based ICA and directivity-dependency-reduced binary masking

A binaural hearing-aid system based on two-stage blind sound source separation (BSS) method is now being studied by some of the authors. This system consists of two techniques; a single-input multiple-output (SIMO)-model-based independent component analysis part and binaural-output-capable SIMO-model-based binary masking (SIMO-BM) parts. However owing to user-specific head-related transfer function (HRTF) for receiving the sounds and adopting SIMO-BMs in the 2nd stage, the previous system has directivity dependency for sound sourcespsila layout. In this paper, we introduce a new BSS method which mitigates user-specific HRTF and keep the separation performance. The experimental results reveal that the separation performance can be improved by using the proposed method compared with the conventional methods in case of sound sources are located lopsidedly.

Fast Convergence Blind Source Separation Using Frequency Subband Interpolation by Null Beamforming

We propose a new algorithm for the blind source separation (BSS) approach in which independent component analysis (ICA) and frequency subband beamforming interpolation are combined. The slow convergence of the optimization of the separation filters is a problem in ICA. Our approach to resolving this problem is based on the relationship between ICA and null beamforming (NBF). The proposed method consists of the following three parts: (I) a frequency subband selector part for learning ICA, (II) a frequency domain ICA part with direction-of-arrivals (DOA) estimation of sound sources, and (III) an interpolation part in which null beamforming constructed with the estimated DOA is used. The results of the signal separation experiments under a reverberant condition reveal that the convergence speed is superior to that of the conventional ICA-based BSS methods.

Fast Convergence Blind Source Separation Based on Frequency Subband Interpolation by Null Beamforming

We propose a new algorithm for blind source separation (BSS) approach that combines independent component analysis (ICA) and frequency subband beamforming interpolation. The slow convergence of the optimization of the separation filters is a problem in ICA. Our approach to resolve this problem is based on the relationship between ICA and null beamforming (NBF). The proposed method consists of the following three parts: (I) a frequency subband selector part for learning ICA, (II) a frequency domain ICA part with direction-of-arrivals (DOA) estimation of sound sources, and (III) an interpolation part using null beamforming constructed with the estimated DOA. In the results of the signal separation experiments under a reverberant condition reveal that the convergence speed is superior to that of the conventional ICA based BSS methods.

ICA and binary-mask-based blind source separation with small directional microphones

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 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, binary mask processing can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results using small directional microphone array reveal that the separation performance can be considerably improved by using the proposed method in comparison to the conventional source separation methods.