Biblio

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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
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., 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
Lladó, P. (2018).  A machine learning approach to violin timbre quality classification. Abstract
Sarasúa, Á., Caramiaux B., & Tanaka A. (2016).  Machine Learning of Personal Gesture Variation in Music Conducting. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 3428–3432.
Yesiler, F., Bozkurt B., & Serra X. (2018).  Makam recognition using extended pitch distribution features and multi-layer perceptrons. Sound and Music Computing Conference (SMC). Abstract
Julià, C. F. (2015).  Making Tabletops Useful with Applications, Frameworks and Multi-Tasking. Department of Information and Communication Technologies. 210. Abstract
application/pdf iconthesis-Julia-Carles-F.pdf
Vinceslas, L., Garcia F., Pérez A., & Maestre E. (2011).  Mapping Blowing Pressure and Sound Features in Recorder Playing. DAFX. 357-360. Abstract
application/pdf icon37_e.pdf
Sarasua, A., Urbano J., & Gomez E. (2019).  Mapping by Observation: Building a User-Tailored Conducting System From Spontaneous Movements. Frontiers on Digital Humanities.
Janer, J., & Maestre E. (2008).  Mapping phonetic features for voice-driven sound synthesis. eBusiness and Telecommunication Networks. CCIS Series. Abstract
Kaltenbrunner, M., & Huxor A. (2001).  Marvin Supporting Awareness through Audio in Collaborative Virtual Environments. Abstract
Jordà, S., & Alonso M. (2006).  Mary had a little scoreTable* or the reacTable* goes melodic. New Interfaces for Musical Expression (NIME). Abstract
Plata Rueda, Á. (2015).  Measurement and computational model of the maximum stable gain in acoustic feedback scenarios. Abstract
application/pdf iconAngel-Plata-Master-thesis-2015.pdf
Mateo Presser, A. (2011).  Measurement of Car Audio Sound Quality using Audio Content Features. Abstract
Papiotis, P., Marchini M., Perez A., & Maestre E. (2014).  Measuring ensemble interdependence in a string quartet through analysis of multidimensional performance data. Frontiers in Psychology. 5, Abstract
Papiotis, P., Marchini M., Maestre E., & Pérez A. (2011).  Measuring ensemble synchrony through violin performance parameters: a preliminary progress report. 2nd Workshop of Social Behavior in Music (SBM-2011/INTETAIN-2011). Abstract
application/pdf iconSBM2011_cameraready.pdf
Troes, T. (2017).  Measuring groove: A Computational Analysis of Timing and Dynamics in Drum Recordings. Abstract
Guastavino, C., Gómez F., Toussaint G., Marandola F., & Gómez E. (2009).  Measuring Similarity between Flamenco Rhythmic Patterns. Journal of New Music Research. 38(2), 129-138. Abstract
application/pdf iconGuastavinoEtAlJNMR2009.pdf
Valero, J. J. (2013).  Measuring Similarity of Automatically Extracted Melodic Pitch Contours for Audio-based Query by Humming of Polyphonic Music Collections. Abstract
application/pdf iconJose-Javier-Valero-Master-Thesis-2013.pdf
Guaus, E., Bonada J., Pérez A., Maestre E., & Blaauw M. (2007).  Measuring the bow pressing force in a real violin performance. International Symposium on Musical Acoustics. Abstract
Serrà, J., Corral A., Boguñá M., Haro G. G., & Arcos J. L. (2012).  Measuring the evolution of contemporary western popular music. Scientific Reports. 2(521),  Abstract
application/pdf iconpost-print
Pérez, A., Bonada J., Maestre E., Guaus E., & Blaauw M. (2008).  Measuring Violin Sound Radiation for Sound Equalization . Acoustics08. Abstract
Bogdanov, D., Porter A., Urbano J., & Schreiber H. (2017).  The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. MediaEval 2017 Workshop. Abstract
Oramas, S., Bogdanov D., & Porter A. (2018).  MediaEval 2018 AcousticBrainz Genre Task: A baseline combining deep feature embeddings across datasets. MediaEval 2018 Workshop.
Bogdanov, D., Porter A., Urbano J., & Schreiber H. (2018).  The MediaEval 2018 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. MediaEval 2018 Workshop. Abstract
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