Plastic multi-resolution auditory model based neural network for speech enhancement

Chen Yen Lai, Yu Wen Lo, Yih Liang Shen, Tai-Shih Chi

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose a plastic auditory model based neural network for speech enhancement. The proposed system integrates a spectro-temporal analytical auditory model with a multi-layer fully-connected network to form a quasi-CNN structure. The initial kernels of the convolutional layer are derived from the neuro-physiological auditory model. To simulate the plasticity of cortical neurons for attentional hearing, the kernels are allowed to adjust themselves pertaining to the task at hand. For the application of speech enhancement, the Fourier spectrogram instead of the auditory spectrogram is used as input to the proposed neural network such that the cleaned speech signal can be well reconstructed. The proposed system performs comparably with standard DNN and CNN systems when plenty resources are available. Meanwhile, under the limited-resource condition, the proposed system outperforms standard systems in all test settings.

原文English
主出版物標題Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面605-609
頁數5
ISBN(電子)9781538615423
DOIs
出版狀態Published - 5 二月 2018
事件9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
持續時間: 12 十二月 201715 十二月 2017

出版系列

名字Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
2018-February

Conference

Conference9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
國家/地區Malaysia
城市Kuala Lumpur
期間12/12/1715/12/17

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