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

arXiv: https://arxiv.org/abs/1904.02992 · HTML: https://mlsanity.com/paper/1904.02992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Pavlok-Nudge: A Feedback Mechanism for Atomic Behaviour Modification with Snoring Usecase](https://mlsanity.com/paper/2305.06110/) (2023)

## Abstract (arXiv)

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.