Snoring / Sleep Apnea Sound

Analysis

Jacob Zurasky ECE5525Fall 2010

Goals

◦ Determine if the principles of speech processing relate to snoring sounds.

◦ Use homomorphic filtering techniques to analyze snoring for pitch and also vocal tract response.

◦ Develop a method to distinguish a simple snore from a sleep apnea event.

Snoring / Sleep Apnea Analysis

Background

Past Research - SRD Store amplitude and frequency spectrum data to SD card

Interface to Sleep Lab polysomnogram equipment

Top Figure is the frequency spectrum (0-2kHz)

Bottom figure is the snore amplitude

SRD – Sample Data Output

Past Research – iPhone App

Assume: s[n] = h[n] * p[n]

FFT -> log( ) -> IFFT, yields the cepstrum

Separate by low quefrency liftering

FFT -> exp( ) -> IFFT, vocal tract response

Homomorphic Filtering – Speech

Assume: s[n] = h[n] * p[n] (palatal flow)

Use sliding hamming window, 50% overlap

Analyze different sounds clips for differences in h[n] and p[n] between normal snoring and an apnea event.

Homomorphic Filtering - Snoring

Signal - apnea1.wav

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Cepstrum – apnea1.wav

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Vocal Tract Response – apnea1

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Signal - apnea2.wav

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Cepstrum – apnea2.wav

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Vocal Tract Response – apnea2

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Signal - snore1.wav

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Signal - snore2.wav

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Observations p[n], ‘Voicing’, of the sleep apnea files has a

much larger magnitude in the cepstral domain.

Vocal tract response during a simple snore is more stable than during an apnea.

Vocal tract response is slower changing during a simple snore.

Redesign the SRD to incorporate the functions of the MATLAB code.

Faster processor, floating point architecture

Continue research to develop a method for in home screening of sleep apnea.

Future Goals