Improved phase aware speech enhancement using bio-inspired and ANN techniques

Abstract

The phase modification of noisy speech signal plays a crucial role in speech enhancement (SE). In the recent past, many speech denoising algorithms have been proposed using the modification of phase information which depends on the scaling factor computed from the noise level. The performance measures of SE is significantly affected by this scaling factor and noise level estimation. However, in these algorithms, the parameters are not optimally tuned for the different noise conditions and also in some cases, the background noise is presumed to be stationary. Further, no earlier attempt has been made to obtain adaptive models which can establish the relationship between noise levels and scaling factor. Being motivated by these observations an attempt has been made in this paper to develop a neural network based model which is capable of properly estimating this scaling factor from the noise level. In the current work, a popular and efficient bio-inspired technique known as firefly algorithm is employed to determine the best possible scaling factor for each noise level. In addition, a relationship is established between noise level and scaling factor using trigonometric functional expansion based artificial neural network. An effective nonstationary noise estimation strategy is also incorporated in the proposed algorithm. Simulation-based experiments are performed to evaluate the effectiveness of the proposed SE algorithm and compared with other six standard SE algorithms using standard database. The analysis of the simulation results demonstrates that the proposed method outperforms the others in terms of both subjective and objective evaluation measures.