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
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.
BibTeX
@inproceedings{icassp2019_deeplearningfeat,
title = {Deep Learning Features for Robust Detection of Acoustic Events in Sleep-disordered Breathing},
author = {Hector E. Romero and Ning Ma and Guy J. Brown and Amy V. Beeston and Madina Hasan},
booktitle = {ICASSP 2019},
year = {2019}
}