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. Abstract
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
Nieves, S. (2019).  Measurement of the Impulse Response of the Guitar to Extract Timbre 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
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.
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