Biblio

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2009
Bischoff, K., Firan C., Paiu R., Nejdl W., Laurier C., & Sordo M. (2009).  Music Mood and Theme Classification a Hybrid Approach. Conference of the International Society for Music Information Retrieval (ISMIR). Abstract
Laurier, C., Sordo M., Serrà J., & Herrera P. (2009).  Music Mood Representations from Social Tags. International Society for Music Information Retrieval (ISMIR) Conference. 381-386. Abstract
Mayor, O., Meyers O., Laurier C., & Koppenberger M. (2009).  MyMTV: A personalized and interactive music channel. Conference of the International Society for Music Information Research (ISMIR), Demo session.. Abstract
Bonada, J. (2009).  Modeling Harmonic Phases at Glottal Closure Instants. International Conference on Digital Audio Effects. Abstract
Pérez, A., & Bonada J. (2009).  Modeling the influence of performance controls on Violin Timbre. Music and Machine Learning workshop at the European Conference on Machine Learning. Abstract
Laurier, C., Meyers O., Serrà J., Blech M., & Herrera P. (2009).  Music Mood Annotator Design and Integration. 7th International Workshop on Content-Based Multimedia Indexing. Abstract
Wack, N., Guaus E., Laurier C., Meyers O., Marxer R., Bogdanov D., et al. (2009).  Music type groupers (MTG): generic music classification algorithms. Music Information Retrieval Evaluation eXchange (MIREX) extended abstract. Abstract
Herrera, P. (2009).  Música y Persuasión. La música y su reflejo en la sociedad. 27-38. Abstract
2008
Laurier, C., Grivolla J., & Herrera P. (2008).  Multimodal Music Mood Classification using Audio and Lyrics. International Conference on Machine Learning and Applications. Abstract
Ponce León, P., Rizo D., & Ramirez R. (2008).  Melody Characterization by a Fuzzy Rule System. International Workshop on Machine Learning and Music, International Conference on Machine Learning. Abstract
Ramirez, R., Pérez A., Maestre E., Kersten S., Rizo D., Roman P., et al. (2008).  Modeling Celtic Violin Expressive Performance. International Workshop on Machine Learning and Music, International Conference on Machine Learning. Abstract
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