Title: Audio acoustic features-based instrument recognition using classification algorithms

Authors: Anuja Arora; Raghav Pangasa; Somya Goel; Tribhuwan Kumar Tewari

Addresses: Jaypee Institute of Information Technology, A-10, Sector-62, Noida, India ' Jaypee Institute of Information Technology, A-10, Sector-62, Noida, India ' Jaypee Institute of Information Technology, A-10, Sector-62, Noida, India ' Jaypee Institute of Information Technology, A-10, Sector-62, Noida, India

Abstract: Musicians have strong knowledge to identify instruments from audio and they can easily categorise music samples by instruments. While on the contrary, instrument recognition is a nascent problem of machine perception area. In this research work, a comparative study of various classification models in order to recognise instrument is presented. Instruments are recognised and classified in the audio on the basis of audio acoustic features. Four machine learning classification algorithms - support vector machine, decision tree, random forest, and ensemble models are applied to classify and tag instruments in audio files based on audio acoustic features. Two well-known datasets, IRMAS and NSynth, are used to apply various classification models and to validate the role of audio acoustic features in instrument recognition. Instrument recognition result shows that the IRMAS dataset achieves maximum accuracy of 74.60% using ensemble model whereas the NSynth dataset gains 96.89% maximum accuracy using the random forest model.

Keywords: decision tree; random forest; support vector machines; SVMs; ensemble; NSynth; IRMAS.

DOI: 10.1504/IJAIP.2021.117615

International Journal of Advanced Intelligence Paradigms, 2021 Vol.20 No.1/2, pp.190 - 209

Received: 30 Jul 2018
Accepted: 07 Mar 2019
Published online: 16 Sep 2021 *

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