A spectrally-based signal subspace approach for speech enhancement

The signal subspace approach for enhancing speech signals degraded by uncorrelated additive noise is studied. The underlying principle is to decompose the vector space of the noisy signal into a signal plus noise subspace and a noise subspace. Enhancement is performed by removing the noise subspace and estimating the clean signal from the remaining signal subspace. The decomposition can theoretically be performed by applying the Karhunen-Loeve transform to the noisy signal. Linear estimation of the clean signal is performed using a perceptually meaningful estimation criterion. The estimator is designed by minimizing signal distortion for a fixed desired spectrum of the residual noise. This criterion enables masking of the residual noise by the speech signal. The filter is implemented as a gain function which modifies the KLT components corresponding to the signal subspace. The gain function is solely dependent on the desired spectrum of the residual noise. Listening tests indicate that 14 out of 16 listeners strongly preferred the proposed approach over the spectral subtraction approach.