Analysis of dual-channel ICA-based blocking matrix for improved noise estimation
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Analysis of dual-channel ICA-based blocking matrix for improved noise estimation Yuanhang Zheng* , Klaus Reindl and Walter Kellermann
Abstract For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, ‘Directional Blind Source Separation (BSS)’ was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated. 1 Introduction Blind signal extraction (BSE) aiming at extracting one source signal from a mixture of an unknown number of acoustic sources in noisy environments is a generic task in acoustic signal processing. It has a wide range of applications in many fields: As popular examples, handsfree interfaces for acoustic communications and humanmachine interaction offer many challenging and relevant application scenarios, such as teleconferencing, interactive television, humanoid robots, and gaming. Moreover, acoustic signal extraction techniques are also highly relevant for assistive devices, such as hearing aids. If multiple microphones are available, data-dependent multichannel approaches for signal extraction can be classified into unsupervised and supervised approaches. The class of unsupervised methods does not require prior knowledge on the spatial distribution of sources and sensors. The lack of prior knowledge is compensated by exploiting fundamental signal characteristics. Conventional unsupervised signal extraction approaches *Correspondence: [email protected] Chair of Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, Cauerstraße 7, Erlangen 91054, Germany
are, e.g., independent component analysis (ICA)-based [1,2] or sparseness-based blind source separation (BSS) algorithms [3,4]. However, conventional ICA-based approaches cannot be used for underdetermined cases, where the number of sensors is less than the number of sources, and sparseness-based
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