Biblio

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R
Ramesh, A. (2009).  Singing Voice Nasality Detection in Polyphonic Audio. Abstract
Ramires, A., & Serra X. (2019).  Data augmentation for instrument classification robust to audio effects. 22nd International Conference on Digital Audio Effects (DAFx-19). Abstract
Ramirez, R., Hazan A., Maestre E., & Serra X. (2006).  A Machine Learning Approach to Expressive Performance in Jazz Standards. (Petrushin, V. A., Khan, L., Ed.).Multimedia Data Mining and Knowledge Discovery. Abstract
Ramirez, R., Hazan A., Gómez E., Maestre E., & Serra X. (2005).  Discovering Expressive Transformation Rules from Saxophone Jazz Performances. Journal of New Music Research. 34, 319-330. Abstract
Ramirez, R., & Maestre E. (2009).  A Framework for Performer Identification in Audio Recordings. International Workshop on Machine Learning and Music - ECML-PKDD 09. Abstract
Ramirez, R., & Hazan A. (2004).  Rule induction for expressive music performance modeling. Abstract
Ramirez, R., Gómez E., Vicente V., Puiggròs M., Hazan A., & Maestre E. (2006).  Modeling Expressive Music Performance in Bassoon Audio Recordings. Intelligent Computing in Signal Processing and Pattern Recognition. 345, 951-957.
Ramirez, R., Maestre E., Pérez A., & Serra X. (2011).  Automatic Performer Identification in Celtic Violin Audio Recordings. Journal of New Music Research. 40(2), 165-174. Abstract
Ramirez, R., & Hazan A. (2005).  Modeling expressive music performance in jazz. International Florida Artificial Intelligence Research Society Conference. Abstract
Ramirez, R., Hazan A., Maestre E., & Serra X. (2008).  A Genetic Rule-based Expressive Performance Model for Jazz Saxophone. Computer Music Journal. 32, 38-50. Abstract
Ramirez, R., Hazan A., Gómez E., & Maestre E. (2004).  Understanding expressive transformations in saxophone jazz performances using inductive machine learning. Sound and Music Computing Conference. Abstract
Ramirez, R., Hazan A., & Maestre E. (2006).  A Sequential Covering Evolutionary Algorithm for Expressive Music Performance. Conference on Innovative Applications of Artificial Intelligence. Abstract
Ramirez, R., & Hazan A. (2004).  Learning Expressive Performance Rules in Jazz.
Ramirez, R., & Hazan A. (2006).  A Tool for Generating and Explaining Expressive Music Performances of Monophonic Jazz Melodies. International Journal on Artificial Intelligence Tools. 15, 673-691. Abstract
Ramirez, R. (2003).  Learning Sets of Musical Rules. Abstract
Ramirez, R., & Puiggròs M. (2006).  Training a Classifier to Detect Instantaneous Musical Cognitive States. International Conference on Music Perception and Cognition. Abstract
Ramirez, R., & Hazan A. (2005).  An Approach to Expressive Music Performance Modeling. 118th Audio Engineering Society Convention. Abstract
Ramirez, R., Pérez A., Kersten S., & Maestre E. (2008).  Performer Identification in Celtic Violin Recordings. International Conference on Music Information Retrieval. Abstract
Ramírez, M., Martinez J., & Santosa A. (2004).  Model checking constraint-based concurrent Java programs. International Workshop on Constructive Methods for Parallel Programming. 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
Ramirez, R., Hazan A., Gómez E., & Maestre E. (2004).  A machine learning approach to expressive performance in jazz standards. ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Abstract
Ramirez, R., & Puiggròs M. (2007).  An Evolutionary Computation Approach to Cognitive States Classification. IEEE Congress on Evolutionary Computing. Abstract
Ramirez, R., Pérez A., Kersten S., & Rizo D. (2010).  Modeling Violin Performances Using Inductive Logic Programming. Intelligent Data Analysis.
Ramirez, R. (2004).  Inductive Logic Programming and Music.
Ramirez, R., Maestre E., Pertusa A., Gómez E., & Serra X. (2007).  Performance-based Interpreter Identification in Saxophone Audio Recordings. IEEE Transactions on Circuits and Systems for Video Technology. 17, 356-364. Abstract
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