An experimental framework for the derivation of perceptually-optimal noise suppression functions

Daniel, Adrien; Lepauloux, Ludovick; Yemdji, Christelle; Evans, Nicholas; Beaugeant, Christophe
ICASSP 2013, 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, May 26-31, Vancouver, Canada

This paper presents a novel experimental framework designed to derive, through subjective testings, noise suppression functions which are perceptually optimal under specific experimental conditions. Noisy speech sequences are continuously processed according
to a gain curve function of the a priori SNR that listeners are required to adjust two points at a time with respect to specified perceptual criteria. An experiment based on this framework is reported testing one specific combination of speech and noise signals. The specified perceptual criterion was the suitability for a phone conversation. The resulting mean experimental gain function shows a statistically significant deviation from an ideal Wiener filter. Experiments based on this framework are repeatable, suit untrained listeners and are considerably faster than conventional subjective testing methods, without the necessity to place restrictive assumptions on the assessed noise suppression function.

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