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A Hierarchical Stratagem for Classification of String Instrument

A Hierarchical Stratagem for Classification of String Instrument

Arijit Ghosal, Suchibrota Dutta, Debanjan Banerjee
Copyright: © 2020 |Volume: 15 |Issue: 1 |Pages: 23
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799803966|DOI: 10.4018/IJWLTT.2020010101
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MLA

Ghosal, Arijit, et al. "A Hierarchical Stratagem for Classification of String Instrument." IJWLTT vol.15, no.1 2020: pp.1-23. http://doi.org/10.4018/IJWLTT.2020010101

APA

Ghosal, A., Dutta, S., & Banerjee, D. (2020). A Hierarchical Stratagem for Classification of String Instrument. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 15(1), 1-23. http://doi.org/10.4018/IJWLTT.2020010101

Chicago

Ghosal, Arijit, Suchibrota Dutta, and Debanjan Banerjee. "A Hierarchical Stratagem for Classification of String Instrument," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 15, no.1: 1-23. http://doi.org/10.4018/IJWLTT.2020010101

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Abstract

Automatic recognition of instrument types from an audio signal is a challenging and a promising research topic. It is challenging as there has been work performed in this domain and because of its applications in the music industry. Different broad categories of instruments like strings, woodwinds, etc., have already been identified. Very few works have been done for the sub-categorization of different categories of instruments. Mel Frequency Cepstral Coefficients (MFCC) is a frequently used acoustic feature. In this work, a hierarchical scheme is proposed to classify string instruments without using MFCC-based features. Chroma reflects the strength of notes in a Western 12-note scale. Chroma-based features are able to differentiate from the different broad categories of string instruments in the first level. The identity of an instrument can be traced through the sound envelope produced by a note which bears a certain pitch. Pitch-based features have been considered to further sub-classify string instruments in the second level. To classify, a neural network, k-NN, Naïve Bayes' and Support Vector Machine have been used.