A subspace method for speech enhancement in the modulation domain
Yu Wang, Mike Brookes · 2013
We present a modulation-domain speech enhancement algo-rithm 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 competi-tive algorithms for colored noise. Index Terms- speech enhancement, subspace, modula-tion domain, covariance matrix estimation