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

Valeria Mordoh1, Yaniv Zigel1

  • 1Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Physiological Measurement
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Summary
This summary is machine-generated.

This study introduces signal processing algorithms to separate individual breathing sounds from noisy recordings, enhancing diagnosis of breathing disorders. The Degenerate Un-mixing Estimation Technique (DUET) demonstrated superior performance in separating audio signals.

Keywords:
audio analysisbreathingbreathing energy ratio measuresleep soundssleeping partnersnoringsource separation

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Acoustics

Background:

  • Home-based sleep evaluations and breathing disorder diagnoses rely on non-contact microphone recordings.
  • Audio signals in these recordings are often distorted by environmental noise and multiple individuals' breathing/snoring sounds.
  • Accurate separation of individual breathing signals is crucial for reliable diagnosis.

Purpose of the Study:

  • To develop and evaluate signal processing and source separation algorithms for enhancing individual breathing/snoring audio signals.
  • To improve the accuracy of at-home sleep and breathing disorder assessments.
  • To address challenges posed by noise and multi-person recordings.

Main Methods:

  • A computer simulation was created using genuine nocturnal recordings from 110 subjects.
  • Two primary source separation techniques were tested: Principal Component Analysis/Independent Component Analysis (PCA/ICA) and Degenerate Un-mixing Estimation Technique (DUET).
  • A beamforming approach was also evaluated alongside PCA/ICA and DUET.

Main Results:

  • DUET achieved the highest Source to Interference Ratio (SIR) of 12.831 dB, outperforming ICA (3.388 dB), PCA (4.452 dB), and beamforming (-0.304 dB).
  • Novel evaluation measures, mel-spectral difference (mel-SID) and breathing energy ratio (BER), confirmed DUET's superiority.
  • DUET's advantage lies in its requirement of only two microphones for effective signal separation.

Conclusions:

  • The developed source separation algorithms, particularly DUET, significantly enhance the quality of individual breathing/snoring audio signals.
  • Improved signal clarity facilitates more informed diagnoses of sleep-related and non-sleep-related diseases.
  • DUET presents a promising, efficient method for improving at-home respiratory monitoring.