Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning
Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning
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DOI：
10.21437/interspeech.2020-2475
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Hao Li;Deliang Wang;Xueliang Zhang;Guanglai Gao
中科院分区：
文献类型：
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作者：
Hao Li;Deliang Wang;Xueliang Zhang;Guanglai Gao
This study investigates deep learning based signal-to-noise ratio (SNR) estimation at the frame level. We propose to employ recurrent neural networks (RNNs) with long short-term memory (LSTM) in order to leverage contextual information for this task. As acoustic features are important for deep learning algorithms, we also examine a variety of monaural features and investigate feature combinations using Group Lasso and sequential floating forward selection. By replacing LSTM with bidirectional LSTM, the proposed algorithm naturally leads to a long-term SNR estimator. Systematical evaluations demonstrate that the proposed SNR estimators significantly outperform other frame-level and long-term SNR estimators.
