2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-52.6
Paper Title CASCADED TIME + TIME-FREQUENCY UNET FOR SPEECH ENHANCEMENT: JOINTLY ADDRESSING CLIPPING, CODEC DISTORTIONS, AND GAPS
Authors Arun Asokan Nair, Johns Hopkins University, United States; Kazuhito Koishida, Microsoft Corporation, United States
SessionSPE-52: Speech Enhancement 8: Echo Cancellation and Other Tasks
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Speech Processing: [SPE-ENHA] Speech Enhancement and Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Speech enhancement aims to improve speech quality by eliminating noise and distortions. While most speech enhancement methods address signal independent additive sources of noise, several degradations to speech signals are signal dependent and non-additive, like speech clipping, codec distortions, and gaps in speech. In this work, we first systematically study and achieve state of the art results on each of these three distortions individually. Next, we demonstrate a neural network pipeline that cascades a time domain convolutional neural network with a time-frequency domain convolutional neural network to address all three distortions jointly. We observe that such a cascade achieves good performance while having the added benefit of keeping the action of each neural network component interpretable.