Abstract
This work describes an algorithm that estimates the corrupting noise power from the speech signal degraded by stationary or highly non-stationary noise sources for the speech enhancement. The proposed technique combines the advantages of minimum statistics and rapid adaptation techniques to address especially low SNRs speech signals. In the first step, the algorithm starts the noise power estimation using minimum statistics principles with a very short adaption window. This yields an overestimation of the noise power that is finally accounted for using recursive averaging techniques. To ensure minimum speech power leakage into estimated noise power the algorithm updates the noise power using an unbiased estimate of the noise power from the minimum statistics approach. To outline the performances of the proposed technique objective and subjective grading tests were conducted for various noise sources at different SNRs.
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Nsabimana, F.X., Subbaraman, V., Zölzer, U. (2011). Corrupting Noise Estimation Based on Rapid Adaptation and Recursive Smoothing. In: Obaidat, M.S., Filipe, J. (eds) e-Business and Telecommunications. ICETE 2009. Communications in Computer and Information Science, vol 130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20077-9_19
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DOI: https://doi.org/10.1007/978-3-642-20077-9_19
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