Memory augmented neural network for source separation

Kai Wei Tsou, Jen-Tzung Chien

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recurrent neural network (RNN) based on long short-term memory (LSTM) has been successfully developed for single-channel source separation. Temporal information is learned by using dynamic states which are evolved through time and stored as an internal memory. The performance of source separation is constrained due to the limitation of internal memory which could not sufficiently preserve long-term characteristics from different sources. This study deals with this limitation by incorporating an external memory in RNN and accordingly presents a memory augmented neural network for source separation. In particular, we carry out a neural Turing machine to learn a separation model for sequential signals of speech and noise in presence of different speakers and noise types. Experiments show that speech enhancement based on memory augmented neural network consistently outperforms that using deep neural network and LSTM in terms of short-term objective intelligibility measure.

Original languageEnglish
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
EditorsNaonori Ueda, Jen-Tzung Chien, Tomoko Matsui, Jan Larsen, Shinji Watanabe
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9781509063413
DOIs
StatePublished - 5 Dec 2017
Event2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan
Duration: 25 Sep 201728 Sep 2017

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2017-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
CountryJapan
CityTokyo
Period25/09/1728/09/17

Keywords

  • Long short-term memory
  • Memory augmented neural network
  • Monaural source separation

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  • Cite this

    Tsou, K. W., & Chien, J-T. (2017). Memory augmented neural network for source separation. In N. Ueda, J-T. Chien, T. Matsui, J. Larsen, & S. Watanabe (Eds.), 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings (pp. 1-6). (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/MLSP.2017.8168120