Improved Musical Instrument Classification Using Cepstral Coefficients and Neural Networks-Reference-Cited by-同舟云学术

Improved Musical Instrument Classification Using Cepstral Coefficients and Neural Networks

Published:2018 Issue: Volume: Page:123-138
ISSN:
Container-title:Methodologies and Application Issues of Contemporary Computing Framework
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Author:

Chakraborty Shruti Sarika,Parekh Ranjan

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-2345-4_10

Reference14 articles.

1. M.S. Nagawade, V.R. Ratnaparkhe, Musical instrument identification using MFCC, in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore (2017), pp. 2198–2202

2. S. Essid, G. Richard, B. David, Instrument recognition in polyphonic music based on automatic taxonomies. IEEE Trans. Audio Speech Lang. Process. 14(1), 68–80 (2006)

3. S.D. Patil, P.S. Sanjekar, Musical instrument identification using SVM, MLP & AdaBoost with formal concept analysis, in 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), Aurangabad (2017), pp. 105–109

4. P.S. Jadhav, Classification of musical instruments sounds by using MFCC and timbral audio descriptors. Int. J. Recent Innov. Trends Comput. Commun (IJRITCC) 3(7), 5001–5006 (2015)

5. M. Muller, D.P.W. Ellis, A. Klapuri, G. Richard, Signal processing for music analysis. IEEE J. Sel. Top. Signal Process. 5(6), 1088–1110 (2011)

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