Tomoya Takatani

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.

Semi-blind suppression of internal noise for hands-free robot spoken dialog system

The speech enhancement architecture presented in this paper is specifically developed for hands-free robot spoken dialog systems. It is designed to take advantage of additional sensors installed inside the robot to record the internal noises. First a modified frequency domain blind signal separation (FD-BSS) gives estimates of the noises generated outside and inside of the robot. Then these noises are canceled from the acquired speech by a multichannel Wiener post-filter. Some experimental results show the recognition improvement for a dictation task in presence of both diffuse background noise and internal noises.

Multistage SIMO-model-based blind source separation combining frequency-domain ICA and time-domain ICA

In this paper, single-input multiple-output (SIMO)-model-based blind source separation (BSS) is addressed, where unknown mixed source signals are detected at the microphones, and these signals can be separated, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. This technique is highly applicable to high-fidelity signal processing such as binaural signal processing. First, we provide an experimental comparison between two kinds of the SIMO-model-based BSS methods, namely, traditional frequency-domain ICA with projection-back processing (FDICA-PB), and SIMO-ICA recently proposed by the authors. Secondly, we propose a new combination technique of the FDICA-PB and SIMO-ICA, which can achieve a higher separation performance in comparison to two methods. The experimental results reveal that the accuracy of the separated SIMO signals in the simple SIMO-ICA is inferior to that of FDICA-PB, but the proposed combination technique can outperform both simple FDICA-PB and SIMO-ICA.

Blind source separation combining SIMO-model-based ICA and adaptive beamforming

A new two-stage blind source separation (BSS) for convolutive mixtures of speech is proposed, in which a single-input multiple-output-model-based ICA (SIMO-ICA) and an adaptive beamforming (ABF) 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. Thus, the separated signals of SIMO-ICA can maintain the spatial qualities of each sound source, and the directions-of-arrival (DOAs) of the sources can be estimated after the separation by SIMO-ICA. Owing to the attractive property, the supervised ABF can be applied to removing the residual interference components efficiently after the SIMO-ICA and DOA estimation procedures. Experimental results reveal that separation performance can be considerably improved by using the proposed method. In addition, the proposed method outperforms the combination of the conventional SIMO-output-type ICA and ABF, as well as both the simple ICA and the simple ABF.

Blind separation of binaural sound mixtures using SIMO-model-based independent component analysis

High-fidelity blind audio signal separation is addressed, adopting the extended ICA algorithm, single-input multiple-output (SIMO)-model-based ICA. The SIMO-ICA consists of multiple ICA parts and a fidelity controller, and each ICA runs in parallel under fidelity control of the entire separation system. 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. Thus, the separated signals of the SIMO-ICA can maintain the spatial qualities of each sound source. We apply the SIMO-ICA to the problem of blind separation of mixed binaural sounds, including the effect of the head-related transfer function (HRTF). Experimental results reveal that the performance of the proposed SIMO-ICA is superior to that of the conventional ICA-based method, and the separated signals of SIMO-ICA maintain the spatial qualities of each sound source.

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

Speech enhancement in presence of diffuse background noise: Why using blind signal extraction?

This paper study the blind estimation of the diffuse background noise for the hands-free speech interface. Some recent papers showed that it is possible to use blind signal separation (BSS) to estimate the diffuse background noise by suppressing the speech component after all the components were separated. In particular, the scale indeterminacy of BSS is avoided by using the projection back method. In this paper, we study an alternative to the projection back for the noise estimation and justify the use of blind signal extraction BSE rather than BSS.

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.