Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

8 Citations (Scopus)
Original languageEnglish
Title of host publicationInternational Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
PublisherIEEE
Publication date2017
ISBN (Electronic)978-1-5090-6341-3
DOIs
Publication statusPublished - 2017
Event2017 IEEE 27th International Workshop on Machine Learning for Signal Processing - Tokyo, Japan
Duration: 25 Sep 201728 Sep 2017
Conference number: 27th
http://mlsp2017.conwiz.dk/home.htm

Conference

Conference2017 IEEE 27th International Workshop on Machine Learning for Signal Processing
Number27th
CountryJapan
CityTokyo
Period25/09/201728/09/2017
Internet address
SeriesIEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings.

Cite this

Kolbæk, M., Yu, D., Tan, Z-H., & Jensen, J. (2017). Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training. In International Workshop on Machine Learning for Signal Processing (MLSP) IEEE. IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings. https://doi.org/10.1109/MLSP.2017.8168152
Kolbæk, Morten ; Yu, Dong ; Tan, Zheng-Hua ; Jensen, Jesper. / Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training. International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2017. (IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings.).
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title = "Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training",
author = "Morten Kolb{\ae}k and Dong Yu and Zheng-Hua Tan and Jesper Jensen",
year = "2017",
doi = "10.1109/MLSP.2017.8168152",
language = "English",
booktitle = "International Workshop on Machine Learning for Signal Processing (MLSP)",
publisher = "IEEE",
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Kolbæk, M, Yu, D, Tan, Z-H & Jensen, J 2017, Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training. in International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings., 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing, Tokyo, Japan, 25/09/2017. https://doi.org/10.1109/MLSP.2017.8168152

Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training. / Kolbæk, Morten; Yu, Dong ; Tan, Zheng-Hua; Jensen, Jesper.

International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2017.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training

AU - Kolbæk, Morten

AU - Yu, Dong

AU - Tan, Zheng-Hua

AU - Jensen, Jesper

PY - 2017

Y1 - 2017

UR - http://www.scopus.com/inward/record.url?scp=85042320147&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2017.8168152

DO - 10.1109/MLSP.2017.8168152

M3 - Article in proceeding

BT - International Workshop on Machine Learning for Signal Processing (MLSP)

PB - IEEE

ER -

Kolbæk M, Yu D, Tan Z-H, Jensen J. Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training. In International Workshop on Machine Learning for Signal Processing (MLSP). IEEE. 2017. (IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings.). https://doi.org/10.1109/MLSP.2017.8168152