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Evaluating Feature Selection Algorithms for Machine Learning-Based Musical Instrument Identification in Monophonic Recordings

Year 2024, , 289 - 301, 31.08.2024

İsmet Emre Yücel , Ulaş Yurtsever

https://doi.org/10.35377/saucis...1516717

Abstract

Musical instrument identification (MII) research has been studied as a subfield of the Music Information Retrieval (MIR) field. Conventional MII models are developed based on hierarchical models representing musical instrument families. However, for MII models to be used in the field of music production, they should be developed based on the arrangement-based functions of instruments in musical styles rather than these hierarchical models. This study investigates how the performance of machine learning based classification algorithms for Guitar, Bass guitar and Drum classes changes with different feature selection algorithms, considering a popular music production scenario. To determine the effect of feature statistics on model performance, Minimum Redundancy Maximum Relevance (mRMR), Chi-sqaure (Chi2), ReliefF, Analysis of Variance (ANOVA) and Kruskal Wallis feature selection algorithms were used. In the end, the neural network algorithm with wide hyperparameters (WNN) achieved the best classification accuracy (91.4%) when using the first 20 statistics suggested by the mRMR and ReliefF feature selection algorithms.

Keywords

Musical instrument identification Audio features Feature selection Machine learning

References

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Year 2024, , 289 - 301, 31.08.2024

İsmet Emre Yücel , Ulaş Yurtsever

https://doi.org/10.35377/saucis...1516717

Abstract

References

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  • [20] M. Chmulik, R. Jarina, M. Kuba, and E. Lieskovska, “Continuous music emotion recognition using selected audio features,” in 2019 42nd International Conference on Telecommunications and Signal Processing, TSP 2019, 2019. doi: 10.1109/TSP.2019.8768806.
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  • [27] K. Avramidis, A. Kratimenos, C. Garoufis, A. Zlatintsi, and P. Maragos, “Deep convolutional and recurrent networks for polyphonic instrument classification from monophonic raw audio waveforms,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2021-June, pp. 3010–3014, 2021, doi: 10.1109/ICASSP39728.2021.9413479.
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There are 72 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

İsmet Emre Yücel 0000-0001-7018-3349

Ulaş Yurtsever 0000-0003-3438-6872

Early Pub Date August 27, 2024
Publication Date August 31, 2024
Submission Date July 15, 2024
Acceptance Date August 21, 2024
Published in Issue Year 2024

Cite

IEEE İ. E. Yücel and U. Yurtsever, “Evaluating Feature Selection Algorithms for Machine Learning-Based Musical Instrument Identification in Monophonic Recordings”, SAUCIS, vol. 7, no. 2, pp. 289–301, 2024, doi: 10.35377/saucis...1516717.

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