Speech Signal Processing

Noise Power Spectral Density Estimation

Here we give Audio examples and the code for the proposed noise power spectral density estimator based on a speech presence probability estimator with fixed priors. The algorithm is proposed in the following papers:

Audio Examples

These audio examples compare single channel noise reduction using Martin's Minimum Statistics [1], the bias compensated MMSE approach [2], and the proposed SPP approach.
The proposed approach is computationally more efficient than the Minimum Statistics [1] or MMSE-BC [2] (approximately factor 4.5 in Matlab) and also requires less memory.

Wiener filter
Noise type noisy proposed Minimum Statistics [1] MMSE-BC [2]
modulated white

audio

audio audio audio
white

audio

audio audio audio
traffic

audio

audio audio audio
babble

audio

audio audio audio
Super-Gaussian filter from [3] with γ=1, ν=0.6
Noise type noisy proposed Minimum Statistics [1] MMSE-BC [2]
modulated white

audio

audio audio audio
white

audio

audio audio audio
traffic

audio

audio audio audio
babble

audio

audio audio

audio

[1] R. Martin, "Noise power spectral density estimation based on optimal smoothing and minimum statistics," IEEE Transactions on Speech and Audio Processing, vol. 9, no. 5, pp. 504-512, Jul. 2001.

[2]  R. C. Hendriks, R. Heusdens, and J. Jensen, "MMSE based noise PSD tracking with low complexity," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4266-4269, Mar. 2010. 

Code

The code for the proposed approach can be found here: [Download]

The code for the MMSE-BC approach [2] can be found here.