Biblio

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Serra, X. (1988).  An Environment for the Analysis, Transformation and Resynthesis of Music Sounds. Abstract
Cano, P., Gómez E., Gouyon F., Herrera P., Koppenberger M., Ong B., et al. (2006).  ISMIR 2004 Audio Description Contest. Abstract
Gómez, E., Streich S., Ong B., Paiva R. P., Tappert S., Batke J. M., et al. (2006).  A Quantitative Comparison of Different Approaches for Melody Extraction from Polyphonic Audio Recordings. Abstract
Salamon, J., Gómez E., & Bonada J. (2011).  Supplementary Graphs: Sinusoid Extraction and Salience Function Design for Predominant Melody Estimation.
application/pdf iconSGB_Graphs.pdf
Cavdir, D., Romaní Picas O., Bandiera G., & Serra X. (2016).  Vibrato Analysis Dataset, Detection and Parameterization. Abstract
PhD Thesis
Roma, G. (2015).  Algorithms and representations for supporting online music creation with large-scale audio databases. Department of Information and Communication Technologies. 181. Abstract
Guaus, E. (2009).  Audio content processing for automatic music genre classification: descriptors, databases, and classifiers. Department of Information and Communication Technologies. Abstract
Gong, R. (2018).  Automatic Assessment of Singing Voice Pronunciation: A Case Study with Jingju Music. Department of Information and Communications Technologies. xxxii + 235. Abstract
Laurier, C. (2011).  Automatic Classification of Musical Mood by Content-Based Analysis. Abstract
Fuhrmann, F. (2012).  Automatic musical instrument recognition from polyphonic music audio signals. Abstract
Şentürk, S. (2016).  Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music. Department of Information and Communication Technologies. 357. Abstract
Özaslan, T. (2013).  Computational Analysis of Expressivity in Classical Guitar Performances. Department of Information and Communication Technologies. 97. Abstract
Gouyon, F. (2005).  A computational approach to rhythm description --- Audio features for the computation of rhythm periodicity functions and their use in tempo induction and music content processing. Department of Information and Communication Technologies. Abstract
Papiotis, P. (2016).  A computational approach to studying interdependence in string quartet performance. Abstract
Gulati, S. (2016).  Computational Approaches for Melodic Description in Indian Art Music Corpora. Department of Information and Communication Technologies. 305. Abstract
Barbosa, A. (2006).  Computer-Supported Cooperative Work for Music Applications. 190. Abstract
Cano, P. (2007).  Content-Based Audio Search from Fingerprinting to Semantic Audio Retrieval. Abstract
Srinivasamurthy, A. (2016).  A Data-driven Bayesian Approach to Automatic Rhythm Analysis of Indian Art Music. Department of Information and Communication Technologies. 332. Abstract
Pons, J. (2019).  Deep Neural Networks for Music and Audio Tagging. Information and Communication Technologies. 216+xxiii. Abstract
Coleman, G. (2016).  Descriptor Control of Sound Transformations and Mosaicing Synthesis. (Bonada, J., Xavier Serra, Ed.).Department of Information and Communication Technologies. 193. Abstract
Jordà, S. (2005).  Digital Lutherie Crafting musical computers for new musics' performance and improvisation. Department of Information and Communication Technologies. Abstract
Pérez, A. (2009).  Enhancing Spectral Synthesis Techniques with Performance Gestures using the Violin as a Case Study. Department of Information and Communication Technologies. 165. Abstract
Salselas, I. (2013).  Exploring interactions between music and language during the early development of music cognition. A computational modelling approach. Department of Information and Communication Technologies. 137. Abstract
Umbert, M. (2016).  Expression Control of Singing Voice Synthesis: Modeling Pitch and Dynamics with Unit Selection and Statistical Approaches. (Bonada, J., Xavier Serra, Ed.).Department of Information and Communication Technologies. 177. Abstract
Bogdanov, D. (2013).  From music similarity to music recommendation: Computational approaches based on audio features and metadata. Department of Information and Communication Technologies. 227.
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