Speech enhancement by sparse, low-rank, and dictionary spectrogram decomposition
Speech enhancement requires some principle by which to distinguish speech and noise, and the most successful separation requires strong models for both speech and noise. If, however, the noise encountered differs significantly from the system's assumptions, performance will suffer. In this work, we propose a novel speech enhancement system based on decomposing the spectrogram into sparse activation of a dictionary of target speech templates, and a low-rank background model, which makes few assumptions about the noise other than its limited spectral variation. A variation of this model specifically designed to handle transient noise intrusions is also proposed. Evaluation via BSS EVAL and PESQ show that the new approaches improve signal-to-distortion ratio in most cases and PESQ in high-noise conditions when compared to several traditional speech enhancement algorithms including log-MMSE.