Acoustic Source Localization by Combination of Supervised Direction-of-Arrival Estimation with Disjoint Component Analysis
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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.
KeywordsIndependent Component Analysis Independent Component Analysis Minimum Variance Distortionless Response Independent Component Analysis Component Sound Object
Supported by DFG grants SFB/TRR 31 “The Active Auditory System” and FOR 1732 “Individualized Hearing Acoustics”.
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