@inproceedings{3955a5f02a2d41e5ac07dccde772dfbf,
title = "DNN-Based Linear Prediction Residual Enhancement for Speech Dereverberation",
abstract = "In daily-life scenarios, reverberation inevitably caus-es a decrease in speech recognizability and speech quality. Exploring methods to eliminate reverberation will benefit both human perception and other speech technology applications such as identity authentication and speech recognition. This paper proposes a speech dereverberation algorithm based on linear prediction (LP) residual processing using deep neural network (DNN). The amplitude spectrum of the LP residual of short-term speech is used as a speech feature to train the DNN, and the mapping relationship between LP residual of the reverberant speech and that of the clean speech is learned. Comparative ex-periments under different reverberation conditions have verified the effectiveness and robustness of the algorithm.",
keywords = "deep neural network, linear prediction resid-ual, speech dereverberation",
author = "Xinyang Feng and Nuo Li and Zunwen He and Yan Zhang and Wancheng Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 APSIPA.; 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 ; Conference date: 14-12-2021 Through 17-12-2021",
year = "2021",
language = "English",
series = "2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "541--545",
booktitle = "2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings",
address = "United States",
}