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Supervised Speech Separation Based on Deep Learning: An Overview.

DeLiang Wang1, Jitong Chen2

  • 1Department of Computer Science and Engineering and the Center for Cognitive and Brain Sciences, The Ohio State University, Columbus, OH 43210 USA, and also with the Center of Intelligent Acoustics and Immersive Communications, Northwestern Polytechnical University, Xi'an 710072, China.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|June 22, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning significantly advances supervised speech separation by learning patterns from data. This overview details deep learning methods for speech enhancement, speaker separation, and dereverberation, improving performance.

Keywords:
Seech separationarray separationbeamformingdeep learningdeep neural networksspeaker separationspeech dereverberationspeech enhancementsupervised speech separationtime-frequency masking

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Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Machine Learning

Background:

  • Speech separation aims to isolate target speech from background noise.
  • Traditionally addressed via signal processing, it's now a supervised learning problem.
  • Deep learning has recently revolutionized supervised speech separation performance.

Purpose of the Study:

  • Provide a comprehensive overview of deep learning-based supervised speech separation.
  • Review monaural and multi-microphone separation algorithms.
  • Discuss generalization and conceptual issues in supervised separation.

Main Methods:

  • Focus on supervised learning approaches for speech separation.
  • Examine deep learning architectures and training methodologies.
  • Categorize methods into speech enhancement, speaker separation, and dereverberation.

Main Results:

  • Deep learning has dramatically accelerated progress and boosted separation performance.
  • Various monaural and multi-microphone techniques have been developed.
  • Generalization remains a key challenge in supervised learning for speech separation.

Conclusions:

  • Deep learning is the current state-of-the-art for supervised speech separation.
  • Understanding learning machines, targets, and features is crucial.
  • Future research should address generalization and define target sources more precisely.