標題: Plastic multi-resolution auditory model based neural network for speech enhancement
作者: Lai, Chen-Yen
Lo, Yu-Wen
Shen, Yih-Liang
Chi, Tai-Shih
電機工程學系
Department of Electrical and Computer Engineering
公開日期: 1-Jan-2017
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.
URI: http://hdl.handle.net/11536/146964
ISSN: 2309-9402
期刊: 2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017)
起始頁: 605
結束頁: 609
Appears in Collections:Conferences Paper