CBSENet: A conformer-based speech enhancement network

Wenke Xu; Xuesong Su; Jin Yang

doi:10.1117/12.3034949

11 July 2024 CBSENet: A conformer-based speech enhancement network

Wenke Xu, Xuesong Su, Jin Yang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100E (2024) https://doi.org/10.1117/12.3034949
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Deep learning for speech enhancement has become a popular trend. With the introduction of attention mechanisms, the field of speech enhancement has seen further advancements. In previous research, Transformer is commonly employed as the core network for speech enhancement due to its attention capabilities, but it has certain limitations in extracting local features. To address these issues, we propose a novel speech enhancement model named CBSENet. This model utilizes the CRN architecture and adopts Conformer as its core network. The Conformer's capabilities in global and local feature extraction have been validated. The CBSENet model demonstrates excellent performance in speech enhancement tasks and outperforms the baseline network in both objective and subjective evaluation metrics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenke Xu, Xuesong Su, and Jin Yang "CBSENet: A conformer-based speech enhancement network", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100E (11 July 2024); https://doi.org/10.1117/12.3034949

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