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arXiv · 2011.04569

Informed Source Extraction With Application to Acoustic Echo Reduction

Mohamed Elminshawi·Wolfgang Mack·Emanuël A. P. Habets
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Abstract

Informed speaker extraction aims to extract a target speech signal from a mixture of sources given prior knowledge about the desired speaker. Recent deep learning-based methods leverage a speaker discriminative model that maps a reference snippet uttered by the target speaker into a single embedding vector that encapsulates the characteristics of the target speaker. However, such modeling deliberately neglects the time-varying properties of the reference signal. In this work, we assume that a reference signal is available that is temporally correlated with the target signal. To take this correlation into account, we propose a time-varying source discriminative model that captures the temporal dynamics of the reference signal. We also show that existing methods and the proposed method can be generalized to non-speech sources as well. Experimental results demonstrate that the proposed method significantly improves the extraction performance when applied in an acoustic echo reduction scenario.

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BibTeXRIS

Mohamed Elminshawi, Wolfgang Mack, Emanuël A. P. Habets. 2020-11-09. Informed Source Extraction With Application to Acoustic Echo Reduction. https://arxiv.org/abs/2011.04569

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