@inproceedings{b0bdd94a5c72423f9a6f7377a5b1c085,
title = "TeeRNN: A Three-Way RNN Through Both Time and Feature for Speech Separation",
abstract = "Recurrent neural networks (RNNs) have been widely used in speech signal processing. Because it is powerful to modeling some sequential information. While most of the networks about RNNs are on frame sight, we propose three-way RNN called TeeRNN which both process the input through the time and the features. According to that, TeeRNN is better to explore the relationship between the features in each frame of encoded speech. As an additional contribution, we also generated a mixture dataset based on LibriSpeech where the devices mismatched and different noises contained making the separation task harder.",
keywords = "Recurrent neural network, Speech processing, Speech separation",
author = "Runze Ma and Shugong Xu",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2020 ; Conference date: 16-10-2020 Through 18-10-2020",
year = "2020",
doi = "10.1007/978-3-030-60636-7_40",
language = "English",
isbn = "9783030606350",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "485--494",
editor = "Yuxin Peng and Hongbin Zha and Qingshan Liu and Huchuan Lu and Zhenan Sun and Chenglin Liu and Xilin Chen and Jian Yang",
booktitle = "Pattern Recognition and Computer Vision - 3rd Chinese Conference, PRCV 2020, Proceedings",
}