Expressive Performance Modeling, Music Cognition, Music Information Retrieval, Audiology.
Publications and Conference presentations
‣ What score markings can say of the synergy between expressive timing and loudness (Vaquero, C., Titov, I. and Honing, H.)
Accepted for ESCOM, 2017
‣ Application of Hidden Markov Models to music performance style classification via timing and loudness features (Vaquero, C. and Chew, E.)
Operations Research Belgium conference (ORBEL), 2016
‣ A quantitative study on seven historically informed performances of Bach’s BWV 1007 Prelude (Vaquero, C.)
Early Music Journal, 2015
‣ Is there and idiosyncrasy threshold in performance? (Vaquero, C.)
SMART Cognitive Science, 2015
‣ Generating expressive timing by combining rhythmic categories and Lindenmayer systems, (Vaquero, C. and Honing, H.)
AISB, Symposium on Computational Creativity, 2014
‣ Improving the description of instrumental sounds by using ontologies and automatic content analysis (Vaquero, C)
Master Thesis, UPF, 2012
- 2017, Guest Lecturer in Computational Models of Music and Language – Masters in Cognitive Science, University of Amsterdam
- 2016 – , Instructor in Psychoacoustics – Masters in Audiology, SAERA (on-line university)
- 2016 – , Instructor in Computer Music – Muziekacademie Den Haag
- 2015, Teaching Assistant in Computational Musicology – Bachelor in Musicology, University of Amsterdam
- 2015, Guest Lecturer in Computational Models of Music and Language – Masters in Cognitive Science, University of Amsterdam
- 2014, Teaching Assistant in Computational Musicology – Bachelor in Musicology, University of Amsterdam
- 2014, Guest Lecturer in Mastering Audio – Aula de Música, Madrid
- 2013, Instructor in Computational tools for music teachers – CDIF, Madrid
PhD Research Topic
Music performance can be represented by a choice of physical variables of which, according to theorists, some are preferred over others (Palmer & Hutchins, 2006). Modeling expressiveness, the added value of a performance why music sounds alive and is interesting to listen to (Timmers & Honing, 2002), is one of the most challenging problems in computer music (Ramirez, Maestre, & Serra, 2012). Some of the reasons are that the individual characteristics of the performance and listening can be analyzed within grouped cultural approaches of different styles and historical periods in music.
This PhD dissertation addresses the study of some of the features and elements that are more relevant in expressive music performance. Aiming to find the limits in the communication of expressiveness by comparing individual interpretations and general principles across performances and listeners, and obtaining from this evaluation, a model of some of the most characteristic expressive features of different interpretations. The model derived will make use of computational techniques based on the perceptual constraints that are observed from listening experiments.
Audio Processing and Analysis, Machine Learning, Artificial Life, Neuroscience, Cognitive Systems, Human Computer Interaction, Cultural Evolution, Real Time Interaction, Swarm Behavior.