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

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

11 Citationer (Scopus)
OriginalsprogEngelsk
TitelInternational Workshop on Machine Learning for Signal Processing (MLSP)
Antal sider6
ForlagIEEE
Publikationsdato2017
ISBN (Elektronisk)978-1-5090-6341-3
DOI
StatusUdgivet - 2017
Begivenhed2017 IEEE 27th International Workshop on Machine Learning for Signal Processing - Tokyo, Japan
Varighed: 25 sep. 201728 sep. 2017
Konferencens nummer: 27th
http://mlsp2017.conwiz.dk/home.htm

Konference

Konference2017 IEEE 27th International Workshop on Machine Learning for Signal Processing
Nummer27th
LandJapan
ByTokyo
Periode25/09/201728/09/2017
Internetadresse
NavnIEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings.

Citer dette

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. I 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",
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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. i International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings., 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. (IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Proceedings.).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer 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

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U2 - 10.1109/MLSP.2017.8168152

DO - 10.1109/MLSP.2017.8168152

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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. I 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