Abstract
Musical instrument classification from an audio file is a very interesting and important topic in machine learning. In this paper, we represent a method to classify a musical instrument from a single audio file of a specific instrument. We focus on classifying six musical instruments that are very popular for Indian subcontinent, basically used to folk songs. It is also helpful for music genre classifiers. A fairly small dataset contains 600 audio files from harmonium, flute, monochord (ektara), cylindrical wooden drum (dhol), tawala, and violin that are classified using MFCC and various types of classifier. MFCCs are based on signal disintegration with the help of a filter bank. The great things of MFCCs over spectrogram is that they try to model the way perceive like frequency. To classify musical instruments, we are used as the k-nearest neighbor and support vector machine classifier with RBF kernel which provides optimum classification ability. A very high accuracy is achieved (97%) on the test set of our generated dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. A. Alim, N.K.A. Rashid, Some Commonly Used Speech Feature Extraction Algorithms, in From Natural to Artificial Intelligence—Algorithms and Applications, ed. by R. Lopez-Ruiz (IntechOpen) (2018, December 12). https://doi.org/10.5772/intechopen.80419. Available from https://www.intechopen.com/books/from-natural-to-artificial-intelligence-algorithms-and-applications/some-commonly-used-speech-feature-extraction-algorithms
S. McAdams, Musical timbre perception. Psychol Music 35–67 (2013)
J. Burred, A. Roebel, X. Rodet, An Accurate Timbre Model for Musical Instruments and its. in Application to Classification Workshop on Learning the Semantics of Audio Signals (Athens, Greece, pp. 1–1, December 2006). https://hal.archives-ouvertes.fr/hal-01161413
G. Agostini, M. Longari, E. Pollastri, Musical instrument timbres classification with spectral features. EURASIP J. Adv. Signal Process. 2003(1), 943279 (2003)
A. Eronen, “Comparison of features for musical instrument recognition, in Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics (Cat. No. 01TH8575) (IEEE, 2001), pp. 19–22
L. Guignard, G. Kehoe, Learning Instrument Identification (Stanford University CS229 Machine Learning Autumn, 2015)
P. Singh, D. Bachhav, O. Joshi, N. Patil, Musical instrument recognition using CNN and SVM. Int. Res. J. Eng. Technol. (IRJET) 6(3) (2019)
A. Azarloo, F. Farokhi, Automatic Musical Instrument Recognition Using K-NN and MLP Neural Networks (Faculty of Electrical and Electronics Engineering Islamic Azad)
https://hal.archives-ouvertes.fr/hal-02270931 Contributor: PatrickSusini patrick.susini@ircam.fr Submitted on: Monday (2019 September 2). (2:19:52 PM)
U. Oberst, The fast fourier transform. SIAM J. Control Optim. 46(2), 496–540 (2007)
https://sebastianraschka.com/Articles/2014_about_feature_scaling.html#bottom-up-approach
Acknowledgements
We would like to thank DIU NLP and Machine Learning Lab, CSE Department.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anuz, H., Masum, A.K.M., Abujar, S., Hossain, S.A. (2021). Musical Instrument Classification Based on Machine Learning Algorithm. In: Tavares, J.M.R.S., Chakrabarti, S., Bhattacharya, A., Ghatak, S. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-15-9774-9_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-9774-9_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9773-2
Online ISBN: 978-981-15-9774-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)