Sparse Music Representation With Source-Specific Dictionaries and Its Application to Signal Separation
We propose a source-specific dictionary approach to efficient music representation, and apply it to separation of music signals that coexist with background noise such as speech or environmental sounds. The basic idea is to determine a set of elementary functions, called atoms, that efficiently capture music signal characteristics. There are three steps in the construction of a source-specific dictionary. First, we decompose basic components of musical signals (e.g., musical notes) into a set of source-independent atoms (i.e., Gabor atoms). Then, we prioritize these Gabor atoms according to their approximation capability to music signals of interest. Third, we use the prioritized Gabor atoms to synthesize new atoms to build a compact dictionary. The number of atoms needed to represent music signals using the source-specific dictionary is much less than that of the Gabor dictionary, resulting in a sparse music representation. For the single-channel music signal separation, we project the mixture signal onto source-specific atoms. Experimental results are given to demonstrate the efficiency and applications of the proposed approach.