Audio source separation to reduce sleeping partner sounds: a simulation study

  title={Audio source separation to reduce sleeping partner sounds: a simulation study},
  author={Valeria Mordoh and Yaniv Zigel},
  journal={Physiological Measurement},
Objective. When recording a subject in an at-home environment for sleep evaluation or for other breathing disorder diagnoses using non-contact microphones, the breathing recordings (audio signals) can be distorted by sounds such as TV, outside noise, or air-conditioners. If two people are sleeping together, both may produce breathing/snoring sounds that need to be separated. In this study, we present signal processing and source separation algorithms for the enhancement of individual breathing… 
2 Citations

Unique Methods for Determining the Attenuation and Delay in Blind Source Separation Based on the Degenerate Unmixing Estimation Technique

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  • J. ThiemannE. Vincent
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    2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
  • 2013
An experimental comparison of some established beamforming and post-filtering techniques on the one hand and modern BSS techniques involving advanced spectral models on the other hand shows that, provided that a suitable post-filter or spectral model is chosen, beamforming performs similar to BSS on average in the single-spe Speaker case while in the two-speaker case BSS exceeds beamformer performance.

Snorer Diarisation Based On Deep Neural Network Embeddings

  • H. E. RomeroNing MaGuy J. Brown
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    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2020
A novel acoustic analysis system for snorer diarisation, a concept extrapolated from speaker diarised research, which allows screening for SDB of both the user and the bed partner using a single smartphone.

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This study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology that can accurately discriminate between snore and non-snore acoustic events.

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  • W. LiaoYi-Syuan Su
  • Computer Science
    18th International Conference on Pattern Recognition (ICPR'06)
  • 2006
The classification of audio signals recorded in all-night sleep studies is described to separate the episodes into snoring sounds and non-snoring sounds, and hierarchical classification schemes are employed to classify sounds into human sounds andnon-human sounds.

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