STFT-Domain Neural Speech Enhancement With Very Low Algorithmic Latency-Reference-Cited by-同舟云学术

STFT-Domain Neural Speech Enhancement With Very Low Algorithmic Latency

Published:2023 Issue: Volume:31 Page:397-410
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Wang Zhong-Qiu¹^ORCID,Wichern Gordon²^ORCID,Watanabe Shinji¹^ORCID,Le Roux Jonathan²^ORCID

Affiliation:

1. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA

2. Mitsubishi Electric Research Laboratories, Cambridge, MA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/9970249/09961936.pdf?arnumber=9961936

Reference65 articles.

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2. Beam-Guided TasNet: An Iterative Speech Separation Framework with Multi-Channel Output

3. Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions

4. Demystifying TasNet: A Dissecting Approach

5. On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments

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