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Most DFT domain based speech enhancement methods are dependent on an estimate of the noise power spectral density (PSD). For non-stationary noise sources it is desirable to estimate the noise PSD also in spectral regions where speech is present. In this paper a new method for noise tracking is presented, based on eigenvalue decompositions of correlation matrices that are constructed from time series of noisy DFT coefficients. 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.