ASSDFeb 7, 2018

A Divide and Conquer Strategy for Musical Noise-free Speech Enhancement in Adverse Environments

arXiv:1802.02665v1
Originality Incremental advance
AI Analysis

This addresses the problem of musical noise in speech enhancement for applications like hearing aids or communication systems, but it is incremental as it builds on existing spectral subtraction methods.

The paper tackles speech enhancement in low-SNR adverse environments by dividing the process into noise and phase compensation steps, resulting in a method that outperforms state-of-the-art approaches in objective measures and listening tests for street or babble noise.

A divide and conquer strategy for enhancement of noisy speeches in adverse environments involving lower levels of SNR is presented in this paper, where the total system of speech enhancement is divided into two separate steps. The first step is based on noise compensation on short time magnitude and the second step is based on phase compensation. The magnitude spectrum is compensated based on a modified spectral subtraction method where the cross-terms containing spectra of noise and clean speech are taken into consideration, which are neglected in the traditional spectral subtraction methods. By employing the modified magnitude and unchanged phase, a procedure is formulated to compensate the overestimation or underestimation of noise by phase compensation method based on the probability of speech presence. A modified complex spectrum based on these two steps are obtained to synthesize a musical noise free enhanced speech. Extensive simulations are carried out using the speech files available in the NOIZEUS database in order to evaluate the performance of the proposed method. It is shown in terms of the objective measures, spectrogram analysis and formal subjective listening tests that the proposed method consistently outperforms some of the state-of-the-art methods of speech enhancement for noisy speech corrupted by street or babble noise at very low as well as medium levels of SNR.

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