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Musical Instrument Recognition and Transcription using Neural Network

Published on March 2014 by V. S. Shelar, D. G. Bhalke
journal_cover_thumbnail

Emerging Trends in Electronics and Telecommunication Engineering 2013
Foundation of Computer Science USA
NCET - Number 1
March 2014
Authors: V. S. Shelar, D. G. Bhalke
e2006b3b-e342-40cb-a3e1-01af829d0a1c

V. S. Shelar, D. G. Bhalke . Musical Instrument Recognition and Transcription using Neural Network. Emerging Trends in Electronics and Telecommunication Engineering 2013. NCET, 1 (March 2014), 31-36.

@article{
author = { V. S. Shelar, D. G. Bhalke },
title = { Musical Instrument Recognition and Transcription using Neural Network },
journal = { Emerging Trends in Electronics and Telecommunication Engineering 2013 },
issue_date = { March 2014 },
volume = { NCET },
number = { 1 },
month = { March },
year = { 2014 },
issn = 0975-8887,
pages = { 31-36 },
numpages = 6,
url = { /proceedings/ncet/number1/15653-1425/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Electronics and Telecommunication Engineering 2013
%A V. S. Shelar
%A D. G. Bhalke
%T Musical Instrument Recognition and Transcription using Neural Network
%J Emerging Trends in Electronics and Telecommunication Engineering 2013
%@ 0975-8887
%V NCET
%N 1
%P 31-36
%D 2014
%I International Journal of Computer Applications
Abstract

In this paper musical instrument recognition and transcription for piano, guitar, violin is discussed. The system is implementing in two stages; first stage is musical instrument recognised using spectral features after recognising instrument musical note is recognised using different frequency estimation methods. Feed forward Neural Network has been used as classifier. The system is implemented for Single Instrument Single Note (SISN), Single Instrument Multiple Note (SIMN) and Multiple Instrument Multiple Note (MIMN). The average accuracy is achieved for three instruments is recorded 80%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Feature Extraction And Music Transcription.