A Fusion of Deep and Shallow Learning to Predict Genres Based on Instrument and Timbre Features
| Autor: | Benedikt Adrian, Jurij Kuzmic, Igor Vatolkin |
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| Rok vydání: | 2021 |
| Předmět: |
Training set
business.industry Computer science Training time Instrument recognition 02 engineering and technology Machine learning computer.software_genre Convolutional neural network 020204 information systems Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence business Timbre computer Interpretability |
| Zdroj: | Artificial Intelligence in Music, Sound, Art and Design ISBN: 9783030729134 EvoMUSART |
| DOI: | 10.1007/978-3-030-72914-1_21 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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