Lossy audio signal compression via structured sparse decomposition and compressed sensing

In this paper, we propose a method for lossy audio signal compression via structured sparse decomposition and compressed sensing (CS). In this method, a least absolute shrinkage and selection operator (LASSO) is employed to sparse and structured decompose the audio signals into tonal and transient layers, and then, both resulting layers are compressed by a CS method. By employing a new penalty term, which takes advantage of the structure information of transform coefficients, the LASSO is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two resulting layers, thus improving the performance of CS. Experimental results showed that the new method provided a better compression performance than conventional methods did.