Deep Denoising Autoencoder Based Post Filtering for Speech Enhancement

Ryandhimas E. Zezario, Jen Wei Huang, Xugang Lu, Yu Tsao, Hsin Te Hwang, Hsin Min Wang

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

In this paper, we present a simple yet effective deep denoising autoencoder (DDAE) based post-filter (DPF) approach for speech enhancement (SE). The DPF is designed to estimate the spectral difference of clean-noisy speech pair based on the enhanced-noisy speech pair. The difference estimated by the DPF approach is then used to compensate the noisy speech to obtain the final enhanced speech. We integrate the proposed DPF approach with one traditional SE method (minimum mean square error) and one deep-learning-based SE method (DDAE). Experiments on various noise types and signal-to-noise-ratio conditions were carried out to test the integrated systems. Results of three standardized objective evaluation metrics and automatic speech recognition (ASR) tests confirm that integrating the proposed DPF can improve the performance in further reducing spectral distortions and enhancing the speech quality and intelligibility.

原文English
主出版物標題2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面373-377
頁數5
ISBN(電子)9789881476852
DOIs
出版狀態Published - 2019 三月 4
事件10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
持續時間: 2018 十一月 122018 十一月 15

出版系列

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

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
國家United States
城市Honolulu
期間18-11-1218-11-15

All Science Journal Classification (ASJC) codes

  • Information Systems

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