Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Indian Musical Instrument Recognition using Modified LPC Features

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 13
Year of Publication: 2015
Authors:
Satish R. Sankaye
Suresh C. Mehrotra
U. S. Tandon
10.5120/21758-4991

Satish R.sankaye, Suresh C.mehrotra and U s Tandon. Article: Indian Musical Instrument Recognition using Modified LPC Features. International Journal of Computer Applications 122(13):6-10, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Satish R.sankaye and Suresh C.mehrotra and U.s. Tandon},
	title = {Article: Indian Musical Instrument Recognition using Modified LPC Features},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {13},
	pages = {6-10},
	month = {July},
	note = {Full text available}
}

Abstract

Indian Classical Music is considered very diverse and distinct area of music across the globe. It has its indistinct melodies especially made up of unique musical instruments. It uses a wide variety of Musical Instruments to achieve this feat. In last two decades, researchers are actively associated with human perception towards the study of Musical Instruments. In this paper, we have proposed an innovative method to classify the Indian Musical Instrument Recognition (IMIR) technique using the Modified Linear Predictor Coefficient (LPC) features. The Classification algorithm has adopted Linear Discriminant Analysis (LDA). The proposed method has been tested with nine kinds of musical instruments. The research project involved the identification of musical sounds with experimental results using the present technique which has an accuracy of 93. 04%.

References

  • Glenn Eric, et al. "Hierarchical Parameterisation and classification for Musical Instrument Recognition" The 11th International Conference on Information Sciences, Signal Processing and their Applications.
  • Eichhoff, Markus, and Claus Weihs. "Musical instrument recognition by high-level features. " Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Springer Berlin Heidelberg, 2012. 373-381.
  • Eronen, Antti, and Anssi Klapuri. "Musical instrument recognition using cepstral coefficients and temporal features. " Acoustics, Speech, and Signal Processing, 2000. ICASSP'00. Proceedings. 2000 IEEE International Conference on. Vol. 2. IEEE, 2000.
  • Giannoulis, Dimitrios, and Anssi Klapuri "Musical instrument recognition in polyphonic audio using missing feature approach. " Audio, Speech, and Language Processing, IEEE Transactions on 21. 9 (2013): 1805-1817.
  • Shah, Jashmin K. , et al. "Robust voiced/unvoiced classification using novel features and gaussian mixture model. " IEEE International Conference on Acoustics, Speech, and Signal Processing. 2004.
  • Zlatintsi, Athanasia, and Petros Maragos. "Multiscale fractal analysis of musical instrument signals with application to recognition. " Audio, Speech, and Language Processing, IEEE Transactions on 21. 4 (2013): 737-748.
  • Hai, Jiang, and Er Meng Joo. "Improved linear predictive coding method for speech recognition. " Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on. Vol. 3. IEEE, 2003.
  • Gunasekaran, S. , and K. Revathy. "Fractal dimension analysis of audio signals for Indian musical instrument recognition. " Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on. IEEE, 2008.
  • Kim, Youngmoo E. , and Brian Whitman. "Singer identification in popular music recordings using voice coding features. " Proceedings of the 3rd International Conference on Music Information Retrieval. Vol. 13. 2002.
  • Delac, Kresimir, Mislav Grgic, and Sonja Grgic. "Independent comparative study of PCA, ICA, and LDA on the FERET data set. " International Journal of Imaging Systems and Technology 15. 5 (2005): 252-260.
  • Delac, Kresimir, Mislav Grgic, and Sonja Grgic. "A comparative study of PCA, ICA, and LDA. " Proc. of the 5th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services. 2005.
  • Toygar, Önsen, and Adnan Acan. "Face recognition using PCA, LDA and ICA approaches on colored images. " Journal Of Electrical & Electronics Engineering 3. 1 (2003): 735-743.
  • Balakrishnama, Suresh, Aravind Ganapathiraju, and Joseph Picone. "Linear discriminant analysis for signal processing problems. " Southeastcon'99. Proceedings. IEEE. IEEE, 1999.
  • Ye, Jieping, Ravi Janardan, and Qi Li. "Two-dimensional linear discriminant analysis. " Advances in neural information processing systems. 2004.
  • Kohavi, Ron and George H. John, "Wrappers for feature subset selection" Artificial Intelligence 97. 1 (1997) : 273-324.
  • Hall, Mark,et al. "The WEKA data mining software: an update. " ACM SIGKDD explorations newsletter 11. 1(2009): 10-18.
  • Shi, Haijian. Best-first decision tree learning. Thesis, The University of Waikato, 2007.