Noise reduction of speech signals using the rank-revealing ULLV decomposition

A recursive approach for nonparametric speech enhancement is developed. The underlying principle is to decompose the vector space of the noisy signal into a signal 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 is performed by applying the rank-revealing ULLV algorithm to the noisy signal. With this formulation, a prewhitening operation becomes an integral part of the algorithm. Linear estimation is performed using a proposed minimum variance estimator. Experiments indicate that the approximative method is able to achieve a satisfactory quality of the reconstructed speech signal comparable with eigenfilter based methods.