Minimum Subspace Noise Tracking for noise Power Spectral Density estimation

Speech enhancement is the processing of speech signals in order to improve one or more perceptual aspects. If the statistics of the clean signal and the noise process are explicitly known, enhancement could be dasiaoptimallypsila accomplished (minimizing a distortion measure between the clean and the estimated signals). In practice however, these statistics are not explicitly available, and the overall enhancement accuracy critically depends on the estimation quality of the unknown statistics. The estimation of noise (and speech) statistics is particularly a critical issue and a challenging problem under non-stationary noise conditions. In this paper, we investigate the noise floor estimation using subspace decomposition. We examine the speech DFT rank limited assumption. We propose a new noise PSD estimation scheme (called Minimum Subspace Noise Tracking (MSNT)). The proposed scheme can be interpreted as a combination of the subspace structure and the minimum statistics tracking. Experimental investigation of the MSNT tracking performance and comparison with the state of the art is also presented.