Title: Convolutional Neural Turing Machine for Speech Separation
Authors: Chien, Jen-Tzung
Tsou, Kai-Wei
電機工程學系
Department of Electrical and Computer Engineering
Keywords: Recurrent neural network;convolutional neural network;neural Turing machine;monaural speech separation
Issue Date: 1-Jan-2018
Abstract: Long short-term memory (LSTM) has been successfully developed for monaural speech separation. Temporal information is learned by using dynamic states which are evolved through time and stored as an internal memory. The spectro-temporal data matrix of mixed signal is flattened as input vectors. There are twofold limitations. First, the internal memory in LSTM could not sufficiently characterize long-term information from different sources. Second, the temporal correlation and frequency neighboring in the flattened vectors were smeared. To deal with these limitations, this paper presents a convolutional neural Turing machine (ConvNTM) where the feature maps of spectro-temporal data are extracted and embedded in an external memory at each time step. ConvNTM aims to preserve the spectro-temporal structure in long sequential signals which is exploited to estimate the separated spectral signals. An addressing mechanism is introduced to continuously calculate the read and write heads to retrieve and update memory slots, respectively. The memory augmented source separation is implemented for single-channel speech enhancement. Experimental results illustrate the superiority of ConvNTM to LSTM, NTM and convolutional LSTM for speech enhancement in terms of short-term objective intelligibility measure.
URI: http://hdl.handle.net/11536/152459
ISBN: 978-1-5386-5627-3
Journal: 2018 11TH INTERNATIONAL SYMPOSIUM ON CHINESE SPOKEN LANGUAGE PROCESSING (ISCSLP)
Begin Page: 81
End Page: 85
Appears in Collections:Conferences Paper