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Low latency sound source separation using convolutional recurrent neural networks

2017pp. 71–75

Citations Over TimeTop 10% of 2017 papers

Gaurav Naithani, Tom Barker, Giambattista Parascandolo, Lars Bramsløw, Niels Henrik Pontoppidan, Tuomas Virtanen

Abstract

Deep neural networks (DNN) have been successfully employed for the problem of monaural sound source separation achieving state-of-the-art results. In this paper, we propose using convolutional recurrent neural network (CRNN) architecture for tackling this problem. We focus on a scenario where low algorithmic delay (<; 10 ms) is paramount, and relatively little training data is available. We show that the proposed architecture can achieve slightly better performance as compared to feedforward DNNs and long short-term memory (LSTM) networks. In addition to reporting separation performance metrics (i.e., source to distortion ratios), we also report extended short term objective intelligibility (ESTOI) scores which better predict intelligibility performance in presence of non-stationary interferers.

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