Open AccessSkewed alpha-stable distributions for modeling and classification of musical instruments
Author(s) -
Mehmet Erdal Özbek,
Mehmet Emre Çek,
F. Acar Savacı
Publication year - 2012
Publication title -
turkish journal of electrical engineering and computer sciences/elektrik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 30
eISSN - 1303-6203
pISSN - 1300-0632
DOI - 10.3906/elk-1102-1031
Subject(s) - feature (linguistics) , pattern recognition (psychology) , set (abstract data type) , support vector machine , computer science , alpha (finance) , wavelet , artificial intelligence , musical , gaussian , feature vector , speech recognition , music information retrieval , mathematics , statistics , linguistics , art , philosophy , construct validity , physics , quantum mechanics , visual arts , programming language , psychometrics
Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades. Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance. In this paper, we propose to use the parameters of skewed alpha-stable distribution of sub-band wavelet coefficients of musical sounds as features and show the effectiveness of this new feature set for musical instrument classification. We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers