@inproceedings{1841a7fe32f1491db44448d64340b107,
title = "Microphone array speech denoising modeled by tensor filtering",
abstract = "This paper proposes a novel speech denoising method based on tensor filtering, in which the microphone array speech signal is constructed by tensor data and processed by tensor filtering model. The multi-microphone signal is represented with three-order tensor space in the way of channel, time and frequency. Noise can be reduced by finding the lower-rank approximation of the three-order tensor with tucker model. MDL (Minimum Description Length) criterion is used to estimate the optimal tensor rank. The performance of the proposed approach is evaluated with objective indexes and listening quality test. The experimental results indicate that the proposed approach has potential ability of retrieving the target signal from noisy microphone array signal.",
keywords = "Low rank approximation, Microphone array, Speech denoising, Tensor filtering",
author = "Jing Wang and Yahui Shan and Shequan Jiang and Xiang Xie",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 ; Conference date: 17-10-2016 Through 20-10-2016",
year = "2017",
month = may,
day = "2",
doi = "10.1109/ISCSLP.2016.7918368",
language = "English",
series = "Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Hsin-Min Wang and Qingzhi Hou and Yuan Wei and Tan Lee and Jianguo Wei and Lei Xie and Hui Feng and Jianwu Dang and Jianwu Dang",
booktitle = "Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016",
address = "United States",
}