Automatic Classification of Woodwind Instrument Families Using the SVM and the RVM Classifiers
Publish place: The first international conference of modern research engineers in electricity and computer
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CBCONF01_0275
تاریخ نمایه سازی: 16 شهریور 1395
Abstract:
In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.
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Authors
Mahmood Abbasi Layegh
Department of Electrical Engineering, Urmia University ,Urmia, West Azarbaijan Province, Iran
Mehdi Chehel Amirani
Department of Electrical Engineering, Urmia University, Urmia ۵۷۱۳۵, Iran
Siamak Haghipour
Department of biomedical engineering, Tabriz Branch ,Islamic Azad University, Tabriz ,Iran
Masoud Gravanchi Zadeh
Department of Electrical Engineering, Tabriz University, Tabriz, Iran
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