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Reseach Article

Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy

by Sumit Kumar Banchhor, Arif Khan
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 3
Year of Publication: 2012
Authors: Sumit Kumar Banchhor, Arif Khan
10.5120/ijais12-450131

Sumit Kumar Banchhor, Arif Khan . Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy. International Journal of Applied Information Systems. 1, 3 ( February 2012), 16-19. DOI=10.5120/ijais12-450131

@article{ 10.5120/ijais12-450131,
author = { Sumit Kumar Banchhor, Arif Khan },
title = { Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 3 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 16-19 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number3/73-0131/ },
doi = { 10.5120/ijais12-450131 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:13.706028+05:30
%A Sumit Kumar Banchhor
%A Arif Khan
%T Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 3
%P 16-19
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (flute, guitar and harmonium), which yields a high recognition rate. A large solo database is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. The basic characteristics are computed in 1sec interval and result shows that the estimation of zero crossing rate and short time energy reflects more effectively the difference in musical instruments.

References
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  3. A. Eronen, A. Klapuri: Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features, Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2000, pp. 753-756
  4. T. Kitahara, M. Goto, H. Okuno: Musical Instrument Identification Based on F0-Dependent Multivariate Normal Distribution, Proc. of the 2003 IEEE Int'l Conf. on Acoustic, Speech and Signal Processing (ICASSP '03), Vol.V, pp.421-424, Apr. 2003
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Index Terms

Computer Science
Information Sciences

Keywords

Musical Instrument classification generalization zero crossing rate short time energy