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AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC

Abstract : Music listening context such as location or activity has been shown to greatly influence the users' musical tastes. In this work, we study the relationship between user context and audio content in order to enable context-aware music recommendation agnostic to user data. For that, we propose a semi-automatic procedure to collect track sets which leverages playlist titles as a proxy for context labelling. Using this, we create and release a dataset of ∼50k tracks labelled with 15 different contexts. Then, we present benchmark classification results on the created dataset using an audio auto-tagging model. As the training and evaluation of these models are impacted by missing negative labels due to incomplete annotations, we propose a sample-level weighted cross entropy loss to account for the confidence in missing labels and show improved context prediction results.
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https://hal.archives-ouvertes.fr/hal-02481374
Contributor : Karim M. Ibrahim <>
Submitted on : Monday, February 17, 2020 - 2:12:31 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:56 PM
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Karim Ibrahim, Jimena Royo-Letelier, Elena Epure, Geoffroy Peeters, Gael Richard. AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020, Barcelona, Spain. ⟨10.5281/zenodo.3648287⟩. ⟨hal-02481374⟩

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