  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.
