@ARTICLE{Bhalke_Daulappa_Guranna_Hybridisation_2016,
 author={Bhalke, Daulappa Guranna and Rao, C. B. Rama and Bormane, Dattatraya},
 volume={vol. 41},
 number={No 3},
 journal={Archives of Acoustics},
 pages={427-436},
 howpublished={online},
 year={2016},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={This paper presents the classification of musical instruments using Mel Frequency Cepstral Coefficients (MFCC) and Higher Order Spectral features. MFCC, cepstral, temporal, spectral, and timbral features have been widely used in the task of musical instrument classification. As music sound signal is generated using non-linear dynamics, non-linearity and non-Gaussianity of the musical instruments are important features which have not been considered in the past. In this paper, hybridisation of MFCC and Higher Order Spectral (HOS) based features have been used in the task of musical instrument classification. HOS-based features have been used to provide instrument specific information such as non-Gaussianity and non-linearity of the musical instruments. The extracted features have been presented to Counter Propagation Neural Network (CPNN) to identify the instruments and their family. For experimentation, isolated sounds of 19 musical instruments have been used from McGill University Master Sample (MUMS) sound database. The proposed features show the significant improvement in the classification accuracy of the system.},
 type={Artykuły / Articles},
 title={Hybridisation of Mel Frequency Cepstral Coefficient and Higher Order Spectral Features for Musical Instruments Classification},
 URL={http://ochroma.man.poznan.pl/Content/102706/PDF/aoa-2016-0042.pdf},
 doi={10.1515/aoa-2016-0042},
 keywords={feature extraction, MFCC, HOS, bispectrum, bicoherence, non-linearity, non-Gaussianity, CPNN, Zero Crossing Rate (ZCR)},
}