Low distortion acoustic noise suppression using a perceptual model for speech signals

Algorithms for the suppression of acoustic noise in speech signals are generally short-time spectral amplitude (STSA) methods such as spectral subtraction. These methods have been effective at reducing or removing background noise, but have a tendency (at low SNR) to add annoying artefacts, such as musical noise, and distortion of the speech signal. By employing an auditory model, psychoacoustic effects such as simultaneous masking can be used to apply spectral modification in a more effective manner, reducing the amount of overall modification necessary. In this way, the artefacts introduced by processing are reduced. The paper proposes a method for significantly improving the reduction in the background acoustic noise in narrowband and wideband speech signals, even at low SNR. We show that the use of a subtraction strategy and psychoacoustic model originally intended for audio signals yields an output signal with little or no audible distortion.