An Adaptive Sparse Algorithm for Synthesizing Note Specific Atoms by Spectrum Analysis, Applied to Music Signal Separation

Abstract

In this paper, a sparse method is proposed to synthesize the note-specific atoms for musical notes of different instruments, and is applied to separate the sounds of two instruments coexisting in a monaural mixture. The main idea is to explore the inherent time structures of the musical notes by a novel adaptive method. These structures are used to synthesize some time-domain functions called note-specific atoms. The note-specific atoms of different instruments are integrated in a global dictionary. In this dictionary, there is only one note-specific atom for each note of any instrument, resulting in a sparse space for each instrument. The signal separation is done by mapping the mixture signal to the global dictionary. The signal related to each instrument is estimated by a summation of the mapped note-specific atoms tagged for that instrument. Experimental results demonstrate that the proposed method improves the quality of signal separation compared to a recently proposed method.

Keywords

• adaptive algorithms
• feature extraction
• gaussian noise
• hyperspectral imaging
• image classification