Presenter: Nicolas Dauban, IRIT, Université de Toulouse, France Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_49.pdf Video: https://youtu.be/nDTguxKNVCY Authors: Nicolas Dauban Abstract: This working note describes the participation of the SAM-IRIT team for the Mediaeval AcousticBrainz Genre Task 2017.

1. Deep Neural Network
in the AcousticBrainz Genre Task

2. Related Works
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3. AcousticBrainz Genre Task
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4. Deep Neural Network: topology
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5. Deep Neural Network: implementation
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6. Features selection
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7. Some compromises
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8. Results per tracks: baseline
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All labels Genre labels
Precision Recall F-score Precision Recall F-score
Allmusic 0.5432 0.1927 0.2491 0.5432 0.4536 0.4808
Discogs 0.506 0.193 0.2686 0.506 0.4163 0.4435
LastFM 0.3335 0.133 0.1841 0.3335 0.3021 0.3121
Tagtraum 0.5037 0.1672 0.2495 0.5037 0.4776 0.4862

9. Results per tracks
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All labels Genre labels
Precision Recall F-score Precision Recall F-score
Allmusic 0.3751 0.1455 0.1813 0.3962 0.3334 0.3513
Discogs 0.3043 0.1243 0.1664 0.3221 0.2674 0.2812
LastFM 0.1845 0.0813 0.1073 0.1937 0.1764 0.1813
Tagtraum 0.2892 0.1214 0.1649 0.3231 0.3055 0.311

10. How to improve these results ?
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11. Thanks for your attention, any questions ?
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