Speech enhancement using the sparse code shrinkage technique

Our work introduces the sparse code shrinkage (SCS) technique as a speech enhancement algorithm that aims at improving the quality of speech perception. SCS is a fairly new statistical technique originally presented to the applied mathematics and image denoising community, but, to our knowledge, its potential for speech enhancement has not yet been exploited. Its application on speech denoising gives rise to a conceptual framework which is quite different from the techniques dominating the speech enhancement domain. SCS originates in applying independent component analysis (ICA) to a large ensemble of clean speech frames, revealing their underlying basis of statistically independent functions. Projecting the frames composing a noisy speech signal on this basis, facilitates the application of Bayesian denoising to each of the resulting independent components individually. The maximum a posteriori (MAP) formulation leads to a soft threshold function optimally adapted to the statistics of each independent component which effectively reduces white and coloured Gaussian noise. Subsequently, an inverse transformation from the ICA-transformed domain back to the time domain reconstructs the enhanced signal