An AFK-SVD Sparse Representation Approach for Speech Signal Processing

Sparse representation is a common issue in many signal processing problems. In speech signal processing, how to sparsely represent a speech signal by dictionary learning for improving transmission efficiency has attracted considerable attention in recent years. K-SVD algorithm for dictionary learning is a typical method. But it requires to know the dictionary size prior to dictionary training. A suitable dictionary size can effectively avoid the problem of under-representation or over-representation, which affects the quality of reconstruction speech significantly. To tackle this problem, an Adaptive dictionary size Feedback filtering K-SVD (AFK-SVD) approach is presented in this paper for dictionary leaning. The proposed method first selects the dictionary size adaptively based on the speech signal feasure prior to dictionary learning, and then filters out the noise caused by over-representation. The approach has two unique features: (1) a learning model is constructed based on the training set specifically for adaptive determination of a range of the dictionary size; and (2) a two-level feedback filter measure is developed for removal of speech distortion caused by over-representation. The speech signals from TIMIT speech data sets are used to demonstrate the presented AFK-SVD approach. Experimental results showed that, in comparison with K-SVD, the proposed AFK-SVD method can improve the quality of the reconstructed speech signal in PESQ by 0.8 and SNR by 3 - 7 dB in average.