HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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 samplelevel weighted cross entropy loss to account for the confidence in missing labels and show improved context prediction results.