Unsupervised speech denoising via perceptually motivated robust principal component analysis

To overcome the shortcomings in the existing sparse and low-rank speech denoising method that the auditory perceptual properties are not fully exploited and the speech distortion is easily perceived,a perceptually motivated robust principal component analysis(ISNRPCA) method is presented.To reflect the nonlinear property for frequency perception of the basilar membrane,cochleagram is utilized as inputs of ISNRPCA.ISNRPCA uses the perceptually meaningful Itakura-Saito measure as its optimization objective function.Moreover,nonnegative constraints are also imposed to regularize the decomposed terms with respect to their physical meaning.We propose an alternating direction method of multipliers(ADMM) to solve the optimization problem of ISNRPCA.ISNRPCA is totally unsupervised,neither the speech nor the noise model needs to be trained beforehand.Experimental results under various noise types and different SNRs demonstrate that ISNRPCA shows promising results for speech denoising.Compared to the state of the art baselines,this method achieves better performance on noise suppression and demonstrates at least comparable intelligibility and overall speech quality.