Biblio
] Filters: Author is Rafael Ramirez [Clear All Filters]
(2006). A Sequential Covering Evolutionary Algorithm for Expressive Music Performance.
Conference on Innovative Applications of Artificial Intelligence. Abstract
(2012). Temporal Control In the EyeHarp Gaze-Controlled Musical Interface.
New Interfaces for Musical Expression (NIME) 2012. Abstract
(2013). Towards a Low Cost Mu-Rhythm Based BCI.
Fifth International Brain-Computer Interface Meeting 2013.
(2006). Training a Classifier to Detect Instantaneous Musical Cognitive States.
International Conference on Music Perception and Cognition. Abstract
(2005). Understanding expressive music performance using genetic algorithms.
Lecture Notes in Computer Science 3449. Abstract
(2004). Understanding expressive transformations in saxophone jazz performances using inductive machine learning.
Sound and Music Computing Conference. Abstract
(2006). Using concatenative synthesis for expressive performance in jazz saxophone.
International Computer Music Conference. Abstract
(2015). Performance to Score Sequence Matching for Automatic Ornament Detection in Jazz Music.
International Conference on New Music Concepts. Abstract
(2010). An approach to predicting bowing control parameter contours in violin performance.
Intelligent Data Analysis. 14(5), 587-599.
(2011). Automatic Performer Identification in Celtic Violin Audio Recordings.
Journal of New Music Research. 40(2), 165-174. Abstract
(2010). Automatic performer identification in commercial monophonic Jazz performances.
Pattern Recognition Letters. 31(12), 1514-1523.
(2005). Discovering Expressive Transformation Rules from Saxophone Jazz Performances.
Journal of New Music Research. 34, 319-330. Abstract
(2009). Expressive Concatenative Synthesis by Reusing Samples from Real Performance Recordings.
Computer Music Journal. 33(4), 23-42. Abstract
(2016). The EyeHarp: A Gaze-Controlled Digital Musical Instrument.
Frontiers in Psychology . 7,
(2008). A Genetic Rule-based Expressive Performance Model for Jazz Saxophone.
Computer Music Journal. 32, 38-50. Abstract
(2016). A Machine learning approach to discover rules for expressive performance actions in jazz guitar music.
Frontiers in Psycology. 7, 1965. Abstract
(2010). Modeling Violin Performances Using Inductive Logic Programming.
Intelligent Data Analysis.
(2015). Musical neurofeedback for treating depression in elderly people.
Frontiers in Neuroscience. 9(354),
(2007). Performance-based Interpreter Identification in Saxophone Audio Recordings.
IEEE Transactions on Circuits and Systems for Video Technology. 17, 356-364. Abstract
(2012). A Rule-Based Evolutionary Approach to Music Performance Modeling.
IEEE Transactions on Evolutionary Computation. 16(1), 96 - 107. Abstract
(2014). The Sense of Ensemble: a Machine Learning Approach to Expressive Performance Modelling in String Quartets.
Journal of New Music Research. 43, 303-317.
(2006). A Tool for Generating and Explaining Expressive Music Performances of Monophonic Jazz Melodies.
International Journal on Artificial Intelligence Tools. 15, 673-691. Abstract
(2016). Computational Modelling of Expressive Music Performance in Jazz Guitar: A Machine Learning Approach.
Department of Information and Communication Technologies. 158.
(2016). Digital Musical Instruments for People with Physical Disabilities.
Department of Information and Communication Technologies. 163.
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