A subspace method for speech enhancement in the modulation domain

We present a modulation-domain speech enhancement algorithm based on a subspace method. We demonstrate that in the modulation domain, the covariance matrix of clean speech is rank deficient. We also derive a closed-form expression for the modulation-domain covariance matrix of colored noise in each frequency bin that depends on the analysis window shape and the noise power spectral density. Using this, we combine a noise power spectral density estimator with an efficient subspace method using a time domain constrained (TDC) estimator of the clean speech spectral envelope. The performance of the novel enhancement algorithm is evaluated using the PESQ measure and shown to outperform competitive algorithms for colored noise.