Jacob Zurasky ECE5525 Fall 2010.  Goals ◦ Determine if the principles of speech processing relate to snoring sounds. ◦ Use homomorphic filtering techniques.

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Presentation transcript:

Jacob Zurasky ECE5525 Fall 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.

 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

 Assume: s[n] = h[n] * p[n]  FFT -> log( ) -> IFFT, yields the cepstrum  Separate by low quefrency liftering  FFT -> exp( ) -> IFFT, vocal tract response

 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.

 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.