Speaker Counting and Separation From Single-Channel Noisy Mixtures-Reference-Cited by-同舟云学术

Speaker Counting and Separation From Single-Channel Noisy Mixtures

Published:2023 Issue: Volume:31 Page:1681-1692
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:

Chetupalli Srikanth Raj¹^ORCID,Habets Emanuël A. P.¹^ORCID

Affiliation:

1. International Audio Laboratories, A Joint Institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer IIS, Erlangen, Germany

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/ielx7/6570655/9970249/10105940.pdf?arnumber=10105940

Reference33 articles.

1. Speech Separation for an Unknown Number of Speakers Using Transformers With Encoder-Decoder Attractors

2. Voice separation with an unknown number of multiple speakers;nachmani;Proc Int Conf Mach Learn,0

3. WHAM!: Extending Speech Separation to Noisy Environments

4. End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors

5. MIR_EVAL: A transparent implementation of common MIR metrics;raffel;Proc Int Soc Music Inf Retrieval Conf,0

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2. Mixture to Mixture: Leveraging Close-Talk Mixtures as Weak-Supervision for Speech Separation;IEEE Signal Processing Letters;2024

3. Superm2m: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Robust Asr;2024

4. Attention-enabled adaptive Markov graph convolution;Neural Computing and Applications;2023-12-23

5. Speaker Counting by Scattered Microphone Array Based on DOA and Eigenvalue Estimations in Adverse Environments;2023 9th International Conference on Signal Processing and Communication (ICSC);2023-12-21