classification and SNR prediction
Problem: Automatic classification
of the acoustical situation, and fast prediction of the local
signal-to-noise ratio (SNR)
Application: VAD for mobile communication,
noise suppression for e.g. hearing instruments
Motivation: Humans can easily detect
and classify different sound sources, e.g., distinguish between
speech and noise. Which features in the acoustic waveform allow
for such impressive skills?
Approach: Modeling neurophysiological
findings on amplitude modulation processing in the auditory system
of mammals yielding spectro-temporal feature patterns. Classification
and SNR prediction with artificial neural networks.
Implementation: Narrow-band SNR
estimation fed into Wiener Filter like noise reduction algorithm.
No assumption about stationarity of the noise and no speech pause
Paper free to download:
Tchorz, J., Kleinschmidt, M., and Kollmeier, B.: 'Noise suppression
based on neurophysiologically motivated SNR estimation for robust
speech recognition', Proceedings of NIPS 2000, in press. Download (zipped ps, 110 kbyte)