Equivalence between Frequency-Domain Blind Source Separation and Frequency-Domain Adaptive Beamforming for Convolutive Mixtures

Araki, Shoko; Makino, Shoji; Hinamoto, Yoichi; Mukai, Ryo; Nishikawa, Tsuyoki; Saruwatari, Hiroshi

doi:10.1155/s1110865703305074

Cited by 79 publications

(37 citation statements)

References 24 publications

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“…The lack of robustness of MVDR and other direction-of-arrival based adaptive beamformers on real data has been known for some time in the audio signal processing community (Gannot et al, 2001;Araki et al, 2003). These beamformers aim to minimize the noise power under the constraint of a unit response in the direction of the speaker.…”

Section: 1 Beamforming and Post-filteringmentioning

confidence: 99%

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

Vincent

Watanabe

Nugraha

et al. 2017

Computer Speech & Language

293

177

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Section: 1 Beamforming and Post-filteringmentioning

confidence: 99%

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

Vincent

Watanabe

Nugraha

et al. 2017

Computer Speech & Language

293

177

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“…Despite the fact that the final effect should be similar to the beamformer [11], this method is based only on statistical properties of the signals coming out from separate sources that are assumed to be statistically independent.…”

Section: Introductionmentioning

confidence: 99%

Convolutive Blind Signal Separation Spatial Effectiveness in Speech Intelligibility Improvement

2011

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Blind signal separation is one of the latest methods to improve the signal to noise ratio. The main objective of blind source separation is the transformation of mixtures of recorded signals to obtain each source signal at the output of the procedure, assuming that they are statistically independent. For acoustic signals it can be concluded that the correct separation is possible only if the source signals are spatially separated. That finding suggests analogies with the classical spatial filtering (beamforming). In this study we analyzed an effect of the angular separation of two source signals (i.e. speech and babble noise) to improve speech intelligibility. For this purpose, we chose the blind source separation algorithm based on the convolutive separation, based on second order statistics only. As a system of sensors a dummy head was used (one microphone inside each ear canal), which simulated two hearing aids of a hearing impaired person. The speech reception threshold, before and after the blind source separation was determined. The results have shown significant improvement in speech intelligibility after applying blind source separation (speach reception threshold fell even more than a dozen dB) in cases where the source signals were angularly separated. However, in cases where the source signals were coming from the same directions, the improvement was not observed. Moreover, the effectiveness of the blind source separation, to a large extent, depended on the relative positions of signal sources in space.

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“…BSS was interpreted from the physical standpoint showing the equivalence between frequency-domain BSS and two sets of microphone array systems, i.e., two sets of adaptive beamformers (ABFs) [6]. Convolutive BSS can be understood as multiple ABFs that generate statistically inde-…”

Section: Discussionmentioning

confidence: 99%

“…We can understand the behavior of BSS as two sets of ABFs [6]. An ABF can create only one null towards the jammer when two microphones are used.…”

Section: Separation Mechanism Of Bssmentioning

confidence: 99%

“…Recently, the ICA method has been investigated in detail, and its mechanisms have been gradually uncovered by using theoretical analysis from the perspective of acoustic signal processing [6] as well as experimental analysis based on impulse response [7]. The mechanism of BSS based on ICA has been shown to be equivalent to that of an adaptive microphone array system, i.e., N sets of adaptive beamformers (ABFs) with an adaptive null directivity aimed in the direction of unnecessary sounds.…”

Section: Introductionmentioning

confidence: 99%

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Audio source separation based on independent component analysis

Makino

Araki

Mukai

et al.

2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)

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This paper introduces the blind source separation (BSS) of convolutive mixtures of acoustic signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. Particular attention is paid to the physical interpretation of BSS from the acoustical signal processing point of view. Frequency-domain BSS is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., adaptive beamformers (ABFs). Although BSS can reduce reverberant sounds to some extent in the same way as ABF, it mainly removes the sounds from the jammer direction. This is why BSS has difficulties with long reverberation in the real world. If sources are not "independent," the dependence results in bias noise when obtaining the correct unmixing filter coefficients. Therefore, the performance of BSS is limited by that of ABF. Although BSS is upper bounded by ABF, BSS has a strong advantage over ABF. BSS can be regarded as an intelligent version of ABF in the sense that it can adapt without any information on the array manifold or the target direction, and sources can be simultaneously active in BSS.

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Equivalence between Frequency-Domain Blind Source Separation and Frequency-Domain Adaptive Beamforming for Convolutive Mixtures

Cited by 79 publications

References 24 publications

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

Convolutive Blind Signal Separation Spatial Effectiveness in Speech Intelligibility Improvement

Audio source separation based on independent component analysis

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