Nonlinear filtering of spectrotemporal modulations in speech enhancement

A monaural noise-suppression algorithm is proposed that nonlinearly manipulates the spectrotemporal modulations of speech as represented in a model of auditory cortical processing. A distinctive aspect of this approach is its consideration of the non-stationary dynamic behavior of speech that is captured using nonlinear filters, thus achieving excellent perceptual quality in the presence of many types of additive noise. Subjective tests demonstrate a significant improvement in the quality of the enhanced speech over the noisy samples even at SNRs as low as -5 dB.