Biblio

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H
Haas, J. (2001).  SALTO - A Spectral Domain Saxophone Synthesizer. Abstract
Haki, B. (2018).  Drums and Bass Interlocking. Abstract
Hansen, K., & Alonso M. (2008).  More DJ techniques on the reactable. NIME08. Abstract
Hansen, K., Alonso M., & Dimitrov S. (2007).  Combining DJ Scratching, Tangible Interfaces And A Physics-Based Model Of Friction Sounds. Abstract
Harley, N. (2014).  Evaluation of set-class similarity measures for tonal analysis. Abstract
application/pdf iconNicholas-Harley-Master-Thesis-2014.pdf
Haro, G. G., Xambó A., Fuhrmann F., Bogdanov D., Gómez E., & Herrera P. (2010).  The Musical Avatar - A visualization of musical preferences by means of audio content description. 5th Audio Mostly Conference: A Conference on Interaction with Sound. Abstract
application/pdf iconavatar_AM2010.pdf
Haro, G. G. (2013).  Statistical Distribution of Common Audio Features. Department of Information and Communication Technologies. 203. Abstract
Haro, G. G. (2008).  Detecting and Describing Percussive Events in Polyphonic Music. Abstract
Haro, G. G., Serrà J., Corral A., & Herrera P. (2012).  Power-Law Distribution in Encoded MFCC Frames of Speech, Music, and Environmental Sound Signals. 21st International World Wide Web Conference (WWW 2012): 4th International Workshop on Advances in Music Information Research (AdMIRe 2012). 895-902.
application/pdf iconharo_etal_p895.pdf
Haro, G. G., Xambó A., Fuhrmann F., Bogdanov D., Gómez E., & Herrera P. (2010).  User Evaluation Questionnaire for The Musical Avatar Project. Abstract
application/pdf iconAvatar_User_Evaluation.pdf
Haro, G. G., & Herrera P. (2009).  From Low-level to Song-level Percussion Descriptors of Polyphonic Music. 10th International Society for Music Information Retrieval Conference. Abstract
Haro, G. G., Serrà J., Herrera P., & Corral A. (2012).  Zipf's Law in Short-Time Timbral Codings of Speech, Music, and Environmental Sound Signals. PLoS ONE. 7(3), e33993. Abstract
Harte, C., Sandler M., Abdallah S., & Gómez E. (2005).  Symbolic Representation Of Musical Chords A Proposed Syntax For Text Annotations. Abstract
Hazan, A. (2010).  Musical Expectation Modelling from Audio: a causal mid-level approach to predictive representation and learning of spectro-temporal events.. Departament de Tecnologies de la Informació i les Comunicacions. 119. Abstract
Hazan, A., Brossier P., Holonowicz P., Herrera P., & Purwins H. (2007).  Expectation Along The Beat A Use Case For Music Expectation Models. International Computer Music Conference. Abstract
Hazan, A., & Ramirez R. (2006).  Modelling expressive performance using consistent evolutionary regression trees. Abstract
Hazan, A. (2006).  Computational Modeling of Expressive Music Performance New Machine Learning Approaches for Dealing With Real-World Data. Abstract
Hazan, A., Holonowicz P., Salselas I., Herrera P., Purwins H., Knast A., et al. (2008).  Poster - Modeling the Acquisition of Statistical Regularities in Tone Sequences. CogSci 2008.
Hazan, A. (2005).  Towards automatic transcription of expressive oral percussive performances. Abstract
Hazan, A., Brossier P., Marxer R., & Purwins P. (2009).  What/when causal expectation modelling applied to percussive audio. The Journal of the Acoustical Society of America, vol. 123, issue 5, p. 3800. Abstract
Hazan, A., Grachten M., & Ramirez R. (2006).  Evolving performance models by performance similarity beyond note-to-note transformations. 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. (2005).  Performing expressive rhythms with billaboop voice-driven drum generator. Abstract
Hazan, A. (2005).  Billaboop real-time voice-driven drum generator. 118th Audio Engineering Society Convention. Abstract
Hazan, A., Holonowicz P., Salselas I., Herrera P., Purwins H., Knast A., et al. (2008).  Modeling the Acquisition of Statistical Regularities in Tone Sequences. 30th Annual Meeting of the Cognitive Science Society. Abstract
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