This paper addresses the problem of noise power spectrum estimation. Existing noise estimation methods cannot perform quite reliably when noise level increasing abruptly (e.g., narrowband noise bursts). To overcome this problem, we improve the time-recursive averaging algorithm based on speech presence probability (SPP), by exploiting the sparseness of speech spectrum. Firstly, we utilize the SPP estimation method based on fixed priors to achieve low SPP estimates at time-frequency bins where speech is absent. Furthermore, a spectral sparseness measure is proposed to adjust the SPP estimates. Experiments show the proposed method can update the noise estimates faster than state-of-the-art approaches in both stationary and nonstationary noise.
Bibliographic reference. Li, Chao / Liu, Wenju (2011): "A noise estimation method based on speech presence probability and spectral sparseness", In INTERSPEECH-2011, 1197-1200.