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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Acoustic Source Localization by Combination of Supervised Direction-of-Arrival Estimation with Disjoint Component Analysis
verfasst von : Jörn Anemüller, Hendrik Kayser
Erschienen in: Latent Variable Analysis and Signal Separation
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Abstract
Analysis and processing in reverberant, multi-source acoustic environments encompasses a multitude of techniques that estimate from sensor signals a spatially resolved “image” of acoustic space, a high-level representation of physical sources that consolidates several source components into a single sound object, and the estimation of filter parameters that would permit enhancement of target and attenuation of interfering signal components.
The contribution of the present manuscript is the introduction of a combination of different algorithms from the field of supervised learning, unsupervised subspace decomposition and multi-channel signal enhancement to accomplish these goals.
Specifically, we propose a system that (1) uses a bank of trained support vector machine classifiers to estimate source activity probability for each spatial position and (2) employs disjoint component analysis (DCA) to obtain from this probabilistic spatial source activity map those components that pertain to individual sound objects. We conclude with a brief outline for (3) estimation of multi-channel filter parameters based on DCA components in order to perform target source enhancement.
We illustrate the proposed method with decomposition results obtained with a four-channel hearing aid geometry setup that comprises two localized sources plus isotropic background noise in an anechoic environment.