Integration of auditory masks into a Locally Competitive Algorithm for sparse representations of audio signals

Abstract

The impact of auditory masking when obtaining sparse representations for audio signals is investigated by integrating a masking model into a Locally Competitive Algorithm. The masking model considers both temporal and frequency domain masking effects. The performance of the new algorithm is verified against the original Locally Competitive Algorithm for different types of sound files. Results show that the new algorithm allows for larger residual error between an input signal and its reconstructed version while maintaining the broadcast quality of the reconstructed version. It does so by shaping the residual error such that this larger error is inaudible.