SASEGAN-TCN: Speech enhancement algorithm based on self-attention generative adversarial network and temporal convolutional network

SASEGAN-TCN: Speech enhancement algorithm based on self-attention generative adversarial network and temporal convolutional network

Traditional unsupervised speech enhancement models often have problems such as non-aggregation of input feature information, which will introduce additional noise during training, thereby reducing the quality of the speech signal. In order to solve the above problems, this paper analyzed the impact...

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Bibliographic Details
Main Authors: Rongchuang Lv, Niansheng Chen, Songlin Cheng, Guangyu Fan, Lei Rao, Xiaoyong Song, Wenjing Lv, Dingyu Yang
Format: Article
Language:English
Published: AIMS Press 2024-02-01
Series:Mathematical Biosciences and Engineering
Subjects:
speech enhancement
deep learning
generative adversarial network
autoencoder
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2024172?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2024172?viewType=HTML

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