Iteratively Refined Multi-Channel Speech Separation
Applied SciencesThe combination of neural networks and beamforming has proven very effective in multi-channel speech separation, but its performance faces a challenge in complex environments.
Xu Zhang +3 more
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Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation. [PDF]
IEEE/ACM Trans Audio Speech Lang Process, 2021 Wang ZQ, Wang P, Wang D.
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A causal and talker-independent speaker separation/dereverberation deep learning algorithm: Cost associated with conversion to real-time capable operation. [PDF]
J Acoust Soc Am, 2021 Healy EW +3 more
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Causal speech enhancement using dynamical-weighted loss and attention encoder-decoder recurrent neural network. [PDF]
PLoS One, 2023 Peracha FK +3 more
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Monaural Speech Dereverberation Using Temporal Convolutional Networks with Self Attention. [PDF]
IEEE/ACM Trans Audio Speech Lang Process, 2020 Zhao Y, Wang D, Xu B, Zhang T.
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DSENet: Directional Signal Extraction Network for Hearing Improvement on Edge Devices. [PDF]
IEEE Access, 2023 Kovalyov A, Patel K, Panahi I.
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A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions. [PDF]
J Acoust Soc Am, 2020 Delfarah M, Liu Y, Wang D.
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A Survey on Low-Latency DNN-Based Speech Enhancement. [PDF]
Sensors (Basel), 2023 Drgas S.
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A Novel Artificial-Intelligence-Based Reverberation-Reduction Algorithm for Cochlear Implants Enhances Speech Intelligibility and User Experience. [PDF]
Ear HearLangerak NC +6 more
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Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods. [PDF]
Trends Hear, 2023 Zheng C +6 more
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