Median tracking in noise subspace for noise floor estimation

Various speech enhancement techniques (e.g. noise suppression, dereverberation) rely on the knowledge of the statistics of the clean signal and the noise process. In practice, however, these statistics are not explicitly available, and the overall enhancement accuracy critically depends on the estimation quality of the unknown statistics. With this respect, subspace based approaches have shown to allow for reduced estimation delay and perform a good tracking vs. final misadjustment tradeoff. For an accurate noise non-stationarity tracking, subspace schemes have the challenge to estimate the correlation of the observed signal from a limited number of samples. In this paper, we propose and investigate a median-search approach to update the noise floor estimate, and alleviate estimation noise artifacts. Experimental investigation of the tracking bias, performance and a comparison with some state-of-the-art techniques are also presented.