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Sleep-disordered breathing (SDB) is a serious and prevalent condition, andacoustic analysis via consumer devices (e.g. smartphones) offers a low-costsolution to screening for it. We present a novel approach for the acousticidentification of SDB sounds, such as snoring, using bottleneck featureslearned from a corpus of whole-night sound recordings. Two types of bottleneckfeatures are described, obtained by applying a deep autoencoder to the outputof an auditory model or a short-term autocorrelation analysis. We investigatetwo architectures for snore sound detection: a tandem system and a hybridsystem. In both cases, a `language model' (LM) was incorporated to exploitinformation about the sequence of different SDB events. Our results show thatthe proposed bottleneck features give better performance than conventionalmel-frequency cepstral coefficients, and that the tandem system outperforms thehybrid system given the limited amount of labelled training data available. TheLM made a small improvement to the performance of both classifiers.

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