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Music feature maps with convolutional neural networks for music genre classification

Sènac, Christine and Pellegrini, Thomas and Mouret, Florian and Pinquier, Julien Music feature maps with convolutional neural networks for music genre classification. (2017) In: International Workshop on Content-Based Multimedia Indexing (CBMI), 19 June 2017 - 21 June 2017 (Florence, Italy).

(Document in English)

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Official URL: https://doi.org/10.1145/3095713.3095733


Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure. But, facing the criticism relating to the difficulty in understanding the underlying relationships that neural networks learn in presence of a spectrogram, we propose to use, as entries of a CNN, a small set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality. With CNNs trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are more efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types reaches 91% accuracy on the GTZAN database.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to ACM editor. The article is available at: https://dl.acm.org/doi/pdf/10.1145/3095713.3095733
HAL Id:hal-02874986
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Deposited On:16 Jun 2020 09:14

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