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A RETURN TO DEREVERBERATION IN THE FREQUENCY DOMAIN USING A JOINT LEARNING APPROACH

Citation Author(s):
Yuying Li, Donald Williamson
Submitted by:
Yuying Li
Last updated:
14 May 2020 - 9:32am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Yuying Li
Paper Code:
4925

Abstract 

Abstract: 

Dereverberation is often performed in the time-frequency domain using mostly deep learning approaches. Time-frequency domain processing, however, may not be necessary when reverberation is modeled by the convolution operation. In this paper, we investigate whether deverberation can be effectively performed in the frequency-domain by estimating the complex frequency response of a room impulse response. More specifically, we develop a joint learning framework that uses frequency-domain estimates of the late reverberant response to assist with estimating the direct and early response. We systematically compare our proposed approach to recent deep learning based approaches that operate in the time-frequency domain. The results show that frequency-domain processing is in fact possible and that it often outperforms time-frequency domain based approaches under different conditions.

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Dataset Files

GRACE_ICASSP2020.v4.pdf

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