Biblio

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2017
Espinosa-Anke, L., Oramas S., Saggion H., & Serra X. (2017).  ELMDist: A vector space model with words and MusicBrainz entities. Workshop on Semantic Deep Learning (SemDeep), collocated with ESWC 2017.
Oramas, S., Nieto O., Sordo M., & Serra X. (2017).  A Deep Multimodal Approach for Cold-start Music Recommendation. 2nd Workshop on Deep Learning for Recommender Systems, at RecSys 2017. Abstract
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
2016
Espinosa-Anke, L., Oramas S., Camacho-Collados J., & Saggion H. (2016).  Finding and Expanding Hypernymic Relations in the Music Domain. 19th International Conference of the Catalan Association for Artificial Intelligence (CCIA). Abstract
Oramas, S., Ostuni V. C., Di Noia T., Serra X., & Di Sciascio E. (2016).  Sound and Music Recommendation with Knowledge Graphs. ACM Transactions on Intelligent Systems and Technology (TIST). 8(2), 1-21. Abstract
Oramas, S., Espinosa-Anke L., Lawlor A., Serra X., & Saggion H. (2016).  Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies. 17th International Society for Music Information Retrieval Conference (ISMIR 2016). 150-156. Abstract
2015
Oramas, S., Sordo M., Espinosa-Anke L., & Serra X. (2015).  A Semantic-based Approach for Artist Similarity. 16th International Society for Music Information Retrieval Conference. 100-106. Abstract
Oramas, S., & Sordo M. (2015).  Knowledge Acquisition from Music Digital Libraries. International Association of Music Libraries and International Musicological Society IAML/IMS Conference. Abstract
2014
2013
2012
Gómez, P., Pikrakis A., Mora J., Díaz-Báñez J. M., Gómez E., Escobar Borrego F., et al. (2012).  Automatic Detection of Melodic Patterns in Flamenco Singing by Analyzing Polyphonic Music Recordings. III Interdisciplinary Conference on Flamenco Research (INFLA) and II International Workshop of Folk Music Analysis (FMA). Abstract
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