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arXiv · 1904.02992

Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing

Hector E. Romero·Ning Ma·Guy J. Brown·Amy V. Beeston·Madina Hasan
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Abstract

Sleep-disordered breathing (SDB) is a serious and prevalent condition, and acoustic analysis via consumer devices (e.g. smartphones) offers a low-cost solution to screening for it. We present a novel approach for the acoustic identification of SDB sounds, such as snoring, using bottleneck features learned from a corpus of whole-night sound recordings. Two types of bottleneck features are described, obtained by applying a deep autoencoder to the output of an auditory model or a short-term autocorrelation analysis. We investigate two architectures for snore sound detection: a tandem system and a hybrid system. In both cases, a `language model' (LM) was incorporated to exploit information about the sequence of different SDB events. Our results show that the proposed bottleneck features give better performance than conventional mel-frequency cepstral coefficients, and that the tandem system outperforms the hybrid system given the limited amount of labelled training data available. The LM made a small improvement to the performance of both classifiers.

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BibTeXRIS

Hector E. Romero, Ning Ma, Guy J. Brown, Amy V. Beeston, Madina Hasan. 2019-04-05. Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing. https://arxiv.org/abs/1904.02992

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