Efficient music representation with content adaptive dictionaries

An efficient music representation based on the matching pursuit (MP) technique with content-adaptive dictionaries (CADs) is investigated in this work. The Gabor atoms are commonly adopted in the MP-based signal representation due to their excellent time-frequency localization property. However, the Gabor dictionary may not yield a concise representation for music signals. Music signals have special characteristics which are specified by pitches and durations of music notes. In this work, we exploit music characteristics to create a set of content-adaptive elementary functions called atoms that efficiently capture the inherent structure of music signals. As a result, we are able to project musical signals onto a subspace spanned by atoms from CAD for a concise and efficient representation. The proposed CAD representation technique is applied to music enhancement with noisy background to demonstrate the power of the proposed representation.