Speech enhancement based on soft audible noise masking and noise power estimation

• We propose a perceptual model based speech enhancement algorithm.
• The proposed algorithm achieves good trade-off between noise suppression and speech distortion.
• Musical noise is mitigated by adapting attenuation gains according to short-term noise amplitude.
• Good performance of the algorithm is confirmed through both objective and subjective tests.

This paper presents a perceptual model based speech enhancement algorithm. The proposed algorithm measures the amount of the audible noise in the input noisy speech based on estimation of short-time spectral power of noise signal, and masking threshold calculated from the estimated spectrum of clean speech. An appropriate amount of noise reduction is chosen based on the result to achieve good noise suppression without introducing significant distortion to the clean speech. To mitigate the problem of “musical noise”, the amount of noise reduction is linked directly to the estimation of short-term noise spectral amplitude instead of noise variance so that the spectral peaks of noise can be better suppressed. Good performance of the proposed speech enhancement system is confirmed through objective and subjective tests.