Ravenscroft, W., Goetze, S. and Hain, T. orcid.org/0000-0003-0939-3464 (2022) Receptive field analysis of temporal convolutional networks for monaural speech dereverberation. In: Proceedings of 30th European Signal Processing Conference (EUSIPCO 2022). 2022 30th European Signal Processing Conference (EUSIPCO), 29 Aug - 02 Sep 2022, Belgrade, Serbia. Institute of Electrical and Electronics Engineers (IEEE) , pp. 80-84. ISBN 9781665467995
Abstract
Speech dereverberation is often an important re-quirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. Temporal convolutional net-works (TCNs) are commonly used for sequence modelling in speech enhancement tasks. A feature of TCNs is that they have a receptive field (RF) dependent on the specific model configuration which determines the number of input frames that can be observed to produce an individual output frame. It has been shown that TCNs are capable of performing dereverberation of simulated speech data, however a thorough analysis, especially with focus on the RF is yet lacking in the literature. This paper analyses dereverberation performance depending on the model size and the RF of TCNs. Experiments using the WHAMR corpus which is extended to include room impulse responses (RIRs) with larger T60 values demonstrate that a larger RF can have significant improvement in performance when training smaller TCN models. It is also demonstrated that TCNs benefit from a wider RF when dereverberating RIRs with larger RT60 values.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 by European Association for Signal Processing (EURASIP). Published by IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | speech; dereverberation; temporal convolutional network; enhancement; sequence modelling; tasnet |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Jul 2022 13:02 |
Last Modified: | 18 Oct 2023 00:13 |
Published Version: | https://ieeexplore.ieee.org/document/9909855 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189111 |