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A Fusion of Deep and Shallow Learning to Predict Genres Based on Instrument and Timbre Features

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Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2021)

Abstract

Deep neural networks have recently received a lot of attention and have very successfully contributed to many music classification tasks. However, they have also drawbacks compared to the traditional methods: a very high number of parameters, a decreased performance for small training sets, lack of model interpretability, long training time, and hence a larger environmental impact with regard to computing resources. Therefore, it can still be a better choice to apply shallow classifiers for a particular application scenario with specific evaluation criteria, like the size of the training set or a required interpretability of models. In this work, we propose an approach based on both deep and shallow classifiers for music genre classification: The convolutional neural networks are trained once to predict instruments, and their outputs are used as features to predict music genres with a shallow classifier. The results show that the individual performance of such descriptors is comparable to other instrument-related features and they are even better for more than half of 19 genre categories.

This work was partly funded by the German Research Foundation (DFG), project 336599081 “Evolutionary optimisation for interpretable music segmentation and music categorisation based on discretised semantic metafeatures”.

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Notes

  1. 1.

    http://theremin.music.uiowa.edu. Accessed on 03.02.2021.

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Vatolkin, I., Adrian, B., Kuzmic, J. (2021). A Fusion of Deep and Shallow Learning to Predict Genres Based on Instrument and Timbre Features. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-72914-1_21

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