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Through the in-depth analysis of the sparse coefficient vectors, this method is capable of generating independent sparse music features that are highly interpretable and have been shown to intuitively express the composition of musical instruments, and capture the variations of emotion in the music. Consequently, this approach has great potential for application in the field of mixed musical instrument composition analysis and other time-varying signal analysis.<\/jats:p>","DOI":"10.3233\/jifs-231290","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T10:09:37Z","timestamp":1692698977000},"page":"7785-7796","source":"Crossref","is-referenced-by-count":2,"title":["An intelligent sparse feature extraction approach for music data component recognition and analysis of hybrid instruments"],"prefix":"10.1177","volume":"45","author":[{"given":"Yi","family":"Liao","sequence":"first","affiliation":[{"name":"Hunan Vocational College of Commerce, Changsha, China"}]},{"given":"Zhen","family":"Gui","sequence":"additional","affiliation":[{"name":"Hunan International Economics University, Changsha, 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