Biblio

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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., 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. (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
Ramirez, R., Pérez A., Kersten S., & Rizo D. (2010).  Modeling Violin Performances Using Inductive Logic Programming. Intelligent Data Analysis.
Ramirez, R., Hazan A., & Maestre E. (2005).  Intra-note Features Prediction Model for Jazz Saxophone Performance. International Computer Music Conference. Abstract
Ramírez, M. (2002).  CLAM Visualization Module Disseny d'un mòdul de visualització OO per a un entorn de processament d'àudio independent de plataforma. Abstract
Ramirez, R., Palencia M., Giraldo S., & Vamvakousis Z. (2015).  Musical neurofeedback for treating depression in elderly people. Frontiers in Neuroscience. 9(354), 
application/pdf iconFrontiersHumNeuFINAL.pdf
Ramírez, M., Martinez J., & Santosa A. (2004).  Constraint-based synchronization and verification of distributed Java programs. Lecture Notes in Computer Science . 3132, Abstract
Ramirez, R. (2008).  An fMRI Study on Attentive Music Listening. The Neurosciences and Music. Abstract
Ramirez, R., & Hazan A. (2005).  Understanding expressive music performance using genetic algorithms. Lecture Notes in Computer Science 3449. Abstract
Ramirez, R., Maestre E., & Serra X. (2012).  A Rule-Based Evolutionary Approach to Music Performance Modeling. IEEE Transactions on Evolutionary Computation. 16(1), 96 - 107. Abstract
Ramirez, R., Maestre E., & Pertusa A. (2007).  Identifying saxophonists from their playing styles. 30th AES Conference. Abstract
Ramirez, R., Maestre E., & Serra X. (2010).  Automatic performer identification in commercial monophonic Jazz performances. Pattern Recognition Letters. 31(12), 1514-1523.
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
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