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Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices. (arXiv:2204.02694v1 [eess.AS])
April 7, 2022, 1:11 a.m. | Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann
cs.LG updates on arXiv.org arxiv.org
This work focuses on online dereverberation for hearing devices using the
weighted prediction error (WPE) algorithm. WPE filtering requires an estimate
of the target speech power spectral density (PSD). Recently deep neural
networks (DNNs) have been used for this task. However, these approaches
optimize the PSD estimate which only indirectly affects the WPE output, thus
potentially resulting in limited dereverberation. In this paper, we propose an
end-to-end approach specialized for online processing, that directly optimizes
the dereverberated output signal. In …
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