Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach
Author:
Affiliation:
1. Communication Acoustics, Carl von Ossietzky Universität Oldenburg, and Cluster of Excellence Hearing4All, Oldenburg, Germany
Funder
Deutsche Forschungsgemeinschaft
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/8782710/10376377/10360275.pdf?arnumber=10360275
Reference45 articles.
1. Deep clustering: Discriminative embeddings for segmentation and separation
2. Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks
3. TF-GRIDNET: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation
4. Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation
5. Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation
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