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I have implemented a basic noise estimator using the minimum statistics method. Noise power estimate is obtained as a minimum of the short time power estimate within a window of subband power samples. In short, you take DFT to get amplitudes, square the amplitudes to get power, and than find minimum power across several frames, hoping that noise is the mimimum.

However, the noise in this case is underestimated due to the fact that minimums are taken.

In the paper "Spectral Subtraction Based on Minimum Statistics" by Rainer, there is an omin factor used to compensate for the underestimation, so that the final noise power P_final = P_estimated * omin.

What I don't understand is the formula for omin (marked under (12) in the paper):

omin = 1 / E{Pmin}

"It follows that the bias of the minimum subband power estimate is proportional to the noise power and that the bias can be compensated by multiplying the minimum estimate with the inverse of the mean."

How is the omin calculated?

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