Signal subspace speech enhancement for audible noise reduction

A novel subspace-based speech enhancement scheme based on a criterion of audible noise reduction is considered. Masking properties of the human auditory system is used to define the audible noise quantity in the eigen-domain. Subsequently, an audible noise reduction scheme is developed based on a signal subspace technique. We derive the eigen-decomposition of the estimated speech autocorrelation matrix with the assumption of white noise and outline the implementation of our proposed scheme. We further extend the scheme to the colored noise case. Simulation results show that our proposed scheme outperforms many existing subspace methods in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ) and informal listening tests.