MIC_FuzzyNET: fuzzy integral based ensemble for automatic classification of musical instruments from audio signals

Sahoo, KK, Hazra, R, Ijaz, MF, Kim, S, Singh, PK and Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348, 2022. MIC_FuzzyNET: fuzzy integral based ensemble for automatic classification of musical instruments from audio signals. IEEE Access. ISSN 2169-3536

Preview

Text 1601155_Mahmud.pdf - Post-print Download (4MB) | Preview

Abstract

Music has been an integral part of the history of humankind with theories suggesting it is more antediluvian than speech itself. Music is an ordered succession of tones and harmonies that produce sounds characterised by melody and rhythm. Our paper proposes an ensemble deep learning musical instrument classification (MIC) framework, named as MIC_FuzzyNET model which aims to classify the dominant instruments present in musical clips. Firstly, the musical data is converted to three different spectrograms: Constant Q-Transform, Semitone Spectrogram and Mel Spectrogram, which is then stacked to form 3 channel 2D data. This stacked spectrogram is fed to transfer learning models namely, EfficientNetV2 and ResNet18 which output the preliminary classification scores. A fuzzy rank ensemble model is finally employed that assigns the classifier ranks, on the testing data in order to achieve final enhanced classification scores which reduces error and biases for the constituent CNN architectures. Our proposed framework has been evaluated on the Persian Classical Music Instrument Recognition (PCMIR) dataset and Instrument Recognition in Musical Audio Signals (IRMAS) dataset. It has achieved considerably high accuracy, making our proposed framework a robust MIC model.

Item Type:	Journal article
Publication Title:	IEEE Access
Creators:	Sahoo, K.K., Hazra, R., Ijaz, M.F., Kim, S., Singh, P.K. and Mahmud, M.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	2022
ISSN:	2169-3536
Identifiers:	Number Type 10.1109/access.2022.3208126 DOI 1601155 Other
Rights:	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	23 Sep 2022 08:46
Last Modified:	23 Sep 2022 08:46
URI:	https://irep.ntu.ac.uk/id/eprint/47094

Actions (login required)

Edit View

Statistics

Views

Downloads