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An Adaptive Sparse Algorithm for Synthesizing Note Specific Atoms by Spectrum Analysis, Applied to Music Signal SeparationAZAMIAN, M. , KABIR, E. , SEYEDIN, S. , MASEHIAN, E.
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adaptive algorithms, feature extraction, gaussian noise, hyperspectral imaging, image classification
signal(18), separation(16), processing(16), audio(12), sparse(11), representation(10), music(10), source(9), speech(8), dictionaries(7)
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About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 103 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02014
Web of Science Accession Number: 000405378100014
SCOPUS ID: 85020131730
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.
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