DFT domain subspace based noise tracking for speech enhancement

Most DFT domain based speech enhancement methods are de-pendent on an estimate of the noise power spectral density (PSD). For non-stationary noise sources it is desirable to es-timate the noise PSD also in spectral regions where speech is present. In this paper a new method for noise tracking is pre-sented, based on eigenvalue decompositions of correlation ma-trices that are constructed from time series of noisy DFT coef-ficients. The presented method can estimate the noise PSD at time-frequency points where both speech and noise are present. In comparison to state-of-the-art noise tracking algorithms the proposed algorithm reduces the estimation error between the estimated and the true noise PSD and improves segmental SNR when combined with an enhancement system with several dB. Index Terms: Speech enhancement, noise tracking, DFT do-main subspace decompositions