Speech enhancement based on a constrained sinusoidal model

In this study we propose an algorithm for enhancement of speech degraded by additive broad-band noise. The algorithm represents speech using a sinusoidal model, where model parameters are estimated iteratively. In order to ensure speech-like characteristics observed in clean speech, the model parameters are restricted to satisfy certain smoothness constraints. The algorithm is evaluated using speech signals degraded by additive white Gaussian noise. Results from both objective and subjective evaluations show considerable improvement over traditional spectral subtraction and Wiener filtering based schemes. In particular, in a subjective AB preference test, where enhanced signals were encoded/decoded with the G729 speech codec, the proposed scheme was preferred over the traditional schemes in more than 5 out of 6 cases for input SNRs ranging from 5-20 dB.