Biblio

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A
Anglade, A., Ramirez R., & Dixon S. (2009).  Genre Classification using Harmony Rules Induced from Automatic Chord Transcriptions. International Society for Music Information Retrieval Conference. Abstract
B
Bantula, H., Giraldo S., & Ramirez R. (2016).  JAZZ ENSEMBLE EXPRESSIVE PERFORMANCE MODELING. ISMIR 2016.
C
Celma, Ò., Ramirez R., & Herrera P. (2005).  Foafing the music: A music recommendation system based on RSS feeds and user preferences. 6th International Conference on Music Information Retrieval (ISMIR). 464-467. Abstract
Celma, Ò., Ramirez R., & Herrera P. (2005).  Getting music recommendations and filtering newsfeeds from FOAF descriptions. 1st Workshop on Scripting for the Semantic Web co-located with the 2nd European Semantic Web Conference). Abstract
D
Dalmazzo, D. C., & Ramirez R. (2017).  Air Violin: A Machine Learning Approach to Fingering Gesture Recognition. MIE’17, November 13, 2017, Glasgow, UK. 4. Abstract
G
Giraldo, S., & Ramirez R. (2014).  Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms. International Computer Music Conference/Sound and Music Computing Conference. Abstract
application/pdf iconSergio_SMC_3.pdf
Giraldo, S., & Ramirez R. (2015).  Performance to Score Sequence Matching for Automatic Ornament Detection in Jazz Music. International Conference on New Music Concepts. Abstract
application/pdf iconICNMC 2015 Giraldo
Giraldo, S., & Ramirez R. (2013).  BRAIN-ACTIVITY-DRIVEN REAL-TIME MUSIC EMOTIVE CONTROL. 3rd International Conference on Music & Emotion (ICME3).
application/pdf iconSergio Giraldo - Brain-Activity-Driven Real-Time Music Emotive Control.pdf
Giraldo, S. (2016).  Computational Modelling of Expressive Music Performance in Jazz Guitar: A Machine Learning Approach. Department of Information and Communication Technologies. 158.
application/pdf iconSergio Giraldo PHD thesis 2016
Giraldo, S., & Ramirez R. (2015).  Computational Modelling of Ornamentation in Jazz Guitar Music. International Symposium in Performance Science. 150-151.
application/pdf iconISPS 2015
Giraldo, S., & Ramirez R. (2016).  A Machine learning approach to discover rules for expressive performance actions in jazz guitar music. Frontiers in Psycology. 7, 1965. Abstract
Giraldo, S., & Ramirez R. (2013).  Real Time Modeling of Emotions by Linear Regression. MML 2013: Internatinal Workshop on Machine Learning and Music, ECML/PKDD .
application/pdf iconGiraldo_mml13proceedings.pdf
Giraldo, S., & Ramirez R. (2012).  Modeling Embellishment, Timing and Energy Expressive Transformations in Jazz Guitar. 5th Internationa Workshop on Machine Learning and Music, Edinburgh, Scotland, UK, 2012. Copyright 2012 by the author(s)/owners..
application/pdf iconMML12_Giraldo_Ramirez.pdf
H
Hazan, A., & Ramirez R. (2006).  Modelling expressive performance using consistent evolutionary regression trees. Abstract
Hazan, A., Ramirez R., Maestre E., Pérez A., & Pertusa A. (2006).  Modelling Expressive Performance A Regression Tree Approach Based on Strongly Typed Genetic Programming. European Workshop on Evolutionary Music and Art. Abstract
Hazan, A., Grachten M., & Ramirez R. (2006).  Evolving performance models by performance similarity beyond note-to-note transformations. Abstract
K
Kersten, S., Maestre E., & Ramirez R. (2007).  Modeling and Synthesis of Expressive Performance. Digital Music Research Network Workshop. Abstract
Kersten, S., Maestre E., & Ramirez R. (2008).  Concatenative Synthesis of Expressive Saxophone Performance. Sound and Music Computing Conference. Abstract
Kroher, N., Gómez E., & Ramirez R. (2013).  Caracterización automática del cante flamenco a base de análisis de grabaciones.. Investigación y Flamenco (INFLA).
M
Maestre, E., Ramirez R., Kersten S., & Serra X. (2009).  Expressive Concatenative Synthesis by Reusing Samples from Real Performance Recordings. Computer Music Journal. 33(4), 23-42. Abstract
Maestre, E., Hazan A., Ramirez R., & Pérez A. (2006).  Using concatenative synthesis for expressive performance in jazz saxophone. International Computer Music Conference. Abstract
Maestre, E., Pérez A., & Ramirez R. (2010).  Gesture sampling for instrumental sound synthesis: violin bowing as a case study. International Computer Music Conference. Abstract
application/pdf iconicmc_maestre_perez_ramirez2010.pdf
Maestre, E., & Ramirez R. (2010).  An approach to predicting bowing control parameter contours in violin performance. Intelligent Data Analysis. 14(5), 587-599.
Marchini, M., Ramirez R., Papiotis P., & Maestre E. (2014).  The Sense of Ensemble: a Machine Learning Approach to Expressive Performance Modelling in String Quartets. Journal of New Music Research. 43, 303-317.
Marchini, M., Ramirez R., Papiotis P., & Maestre E. (2013).  Inducing rules of ensemble music performance: a machine learning approach. 3rd international conference on Music & Emotion, Jyväskylä.
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