Biblio

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2019
Porcaro, L., & Gómez E. (2019).  A Model for Evaluating Popularity and Semantic Information Variations in Radio Listening Sessions. 1st Workshop on the Impact of Recommender Systems (ImpactRS), at the 13th ACM Conference on Recommender Systems (RecSys 2019). Abstract
Bogdanov, D., Won M., Tovstogan P., Porter A., & Serra X. (2019).  The MTG-Jamendo Dataset for Automatic Music Tagging. Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019). Abstract
Porcaro, L., Castillo C., & Gómez E. (2019).  Music recommendation diversity: a tentative framework and preliminary results. 20th annual conference of the International Society for Music Information Retrieval (ISMIR). Abstract
2018
Fernández-Macías, E., Gomez E., Hernández-Orallo J., Loe B. - S., Martens B., Martínez-Plumed F., et al. (2018).  A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work. Workshop on architectures and evaluation for generality, autonomy and progress in AI, IJCAI-ECAI 2018, AAMAS 2018 AND ICML 2018.
Oramas, S., Barbieri F., Nieto O., & Serra X. (2018).  Multimodal Deep Learning for Music Genre Classification. Transactions of the International Society for Music Information Retrieval. 1(1), 4-21. Abstract
2017
Oramas, S., Nieto O., Barbieri F., & Serra X. (2017).  Multi-label Music Genre Classification from Audio, Text and Images Using Deep Features. 18th International Society for Music Information Retrieval Conference (ISMIR 2017). Abstract
Chandna, P., Miron M., Janer J., & Gómez E. (2017).  Monoaural Audio Source Separation Using Deep Convolutional Neural Networks. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA ICA2017).
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