Optimization Scheme of Joint Noise Suppression and Dereverberation Based on Higher-Order Statistics

APSIPA ASC 2012 : Asia-Pacific Signal and Information Processing Association 2012 Annual Summit and Conference, December 3-6, 2012, Hollywood, California, USA.In this paper, we apply the higher-order statistics parameter to automatically improve the performance of blind speech enhancement. Recently, a method to suppress both diffuse background noise and late reverberation part of speech has been proposed combining blind signal extraction and Wiener filtering. However, this method requires a good strategy for choosing the set of its parameters in order to achieve the optimum result and to control the amount of musical noise, which is a common problem in non-linear signal processing. We present an optimization scheme to control the value of Wiener filter coefficients used in this method, which depends on the amount of musical noise generated, measured by higher-order statistics. The noise reduction rate and cepstral distortion are also evaluated to confirm the effectiveness of this scheme

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

Abstract-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

Independent Component Analysis ∗

Blind source separation (BSS) technique using independent component anal-ysis (ICA) for acoustic signals has been developed over the last decade. This technique assumes that the source signals are mutually independent, and can estimate the source signals from the mixed signals without a priori information. Thus, this technique is highly applicable in high-quality hands-free telecommuni-cation system. The conventional ICA-based BSS method is a means of extracting the independent sound source signals as the monaural signals from the mixed sig-nals observed in each input channel, and the separated signals include arbitrary spectral distortions. Consequently, they have a serious drawback in that the sep-arated sounds cannot maintain information about the directivity, localization, reverberation, or spatial qualities of each sound source. These problems prevent any BSS methods from being applied to binaural signal processing or high-fidelity sound reproduction system. In this thesis, firstly, in order to solve the above-mentioned fundamental prob

Single-Input-Multiple-Output モデル ニ モトズク ドクリツ セイブン ブンセキ オ モチイタ コウヒンシツ ブラインド オンゲン ブンリ

https://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100048618&oldid=89510博士 (Doctor)工学 (Engineering)博第534号甲第534号博士（工学）奈良先端科学技術大学院大

Blind Source Separation based on Binaural ICA

ICASSP2003: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 6-10, 2003, Hong Kong, China.We newly propose a novel blind separation framework for binaural acoustic signals based on the extended ICA algorithm, binaural ICA (BICA). The BICA consists of multiple ICA and fidelity controller, and each ICA runs in parallel under the control of the fidelity of the whole separation system. The BICA can separate the mixed signals into not monaural source signals but binaurally heard signals of independent sources. Thus, the separated signals of BICA can maintain spatial qualities of each sound source. In order to evaluate its effectiveness, separation experiments are carried out under a reverberant condition. The experimental results reveal that (1) the signal separation performance of the proposed BICA is the same as that of the conventional ICA-based method; and (2) the spatial quality of the separated sound in BICA is remarkably superior to that of the conventional method, especially for the fidelity of the sound reproduction

Blind Separation of Binaural Sound Mixtures Using SIMO-Model-Based Independent Component Analysis

ICASSP2004: IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, 2004, Quebec, Canada.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