期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：1
页码：275-282
DOI：10.14257/ijsip.2014.7.1.26
出版社：SERSC
摘要：This paper presents musical instrument recognition for isolated music sound signals using hybridization of fractional fourier transform (FRFT) based features with timbrel (acoustic) features using feed forward neural network. The FRFT based features which is named as fractional MFCC are computed by replacing conventional discrete fourier transform in mel frequency cepstral coefficient (MFCC) with discrete FRFT. Hybrid features are obtained by effectively combining Fractional MFCC with timbrel features such as temporal, spectral and cepstral features. Feed forward neural network with back propagation algorithm has been used to test the performance of system and results were compared in terms of recognition accuracy and number of features. Proposed feature out performs over individual and other traditional features proposed in the literature. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system is tested on benchmarked McGill University musical sound database.
