Classification of Musical Instruments using SVM and KNN
S. Prabavathy1, V. Rathikarani2, P. Dhanalakshmi3
1S.Prabavathy*, Research Scholar, Department of Computer and Information Science, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.
2V. Rathikarani, Assistant Professor, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.
3P. Dhanalakshmi, Professor, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 02, 2020. | Manuscript published on May 10, 2020. | PP: 1186-1190 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5836059720/2020©BEIESP | DOI: 10.35940/ijitee.G5836.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automatic classification of musical instruments is a challenging task. Music data classification has become a very popular research in the digital world. Classification of the musical instruments required a huge manual process. This system classifies the musical instruments from a several acoustic features that includes MFCC, Sonogram and MFCC combined with Sonogram. SVM and kNN are two modeling techniques used to classify the features. In this paper, to simply musical instruments classifications based on its features which are extracted from various instruments using recent algorithms. The proposed work compares the performance of kNN with SVM. Identifying the musical instruments and computing its accuracy is performed with the help of SVM and kNN classifier, using the combination of MFCC and Sonogram with SVM a high accuracy rate of 98% achieve in classifying musical instruments. The system tested sixteen musical instruments to find out the accuracy level using SVM and kNN.
Keywords: Musical Instruments Classification, Mel frequency cepstral coefficients (MFCC), Feature extraction, kNearest Neighborhood (kNN), Sonogram, Support Vector Machine (SVM).
Scope of the Article: Classification