Biblio

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Gouyon, F., & Dixon S. (2004).  Dance music classification A tempo-based approach. Abstract
Martel, H., & Miron M. (2017).  Data augmentation for deep learning source separation of HipHop songs. 10th International Workshop on Machine Learning and Music. Abstract
Blaauw, M., Bonada J., & Daido R. (2019).  Data Efficient Voice Cloning for Neural Singing Synthesis. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing.
Arumí, P., Garcia D., & Amatriain X. (2006).  A Data Flow Pattern Language for Audio and Music Computing. Abstract
Serra, X. (2012).  Data Gathering for a Culture Specific Approach in MIR. 21st International World Wide Web Conference (WWW 2012): 4th International Workshop on Advances in Music Information Research (AdMIRe 2012). 867-868. 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
Ganguli, K. K., Gulati S., Rao P., & Serra X. (2016).  Data-driven Exploration of Melodic Structures in Hindustani Music. 17th International Society for Music Information Retrieval Conference (ISMIR 2016). 605-611. Abstract
Wüst, O. (2003).  Database-Driven Systems and Applications for Music Information Management. Abstract
Serra, X. (2008).  De la Cultura Musical a la Cultura del So en la Societat Tecnològica. 2n Congrés Internacional de Música a Catalunya. Abstract
Gomez, E., Blaauw M., Bonada J., Chandna P., & Cuesta H. (2018).  Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners. Keynote speech, 2018 Joint Workshop on Machine Learning for Music. The Federated Artificial Intelligence Meeting (FAIM), a joint workshop program of ICML, IJCAI/ECAI, and AAMAS.
Oramas, S., Nieto O., Sordo M., & Serra X. (2017).  A Deep Multimodal Approach for Cold-start Music Recommendation. 2nd Workshop on Deep Learning for Recommender Systems, at RecSys 2017. Abstract
Vinyes, M., Bonada J., & Loscos A. (2006).  Demixing Commercial Music Productions via Human-Assisted Time-Frequency Masking. Abstract
Musevic, S., & Bonada J. (2013).  Derivative Analysis of Complex Polynomial Amplitude, Complex Exponential with Exponential Damping. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 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
application/pdf iconGraham-Coleman-PhD-thesis-2016.pdf
Herrera, P. (1999).  Descriure els sons un marc conceptual per al desenvolupament d'aplicacions de processament del contingut sonor. Abstract
Bonada, J. (1997).  Desenvolupament d'un entorn gràfic per a l`anàlisi, transformació i síntesi de sons mitjançant models espectrals. Abstract
Mora, A. (2003).  Desenvolupament d'un entorn per a l'anàlisi, transformaciò i síntesi de sons usant la llibreria CLAM.
Casasnovas, I. (2004).  Desenvolupament d'un mòdul interactiu de visualització d'àudio en temps real. Abstract
Blanco, A. (2016).  Design and Evaluation of a Musical Neurofeedback Software in Matlab. Abstract
application/pdf iconAngel-Blanco-Master-thesis-2106.pdf
Font, F. (2010).  Design and Evaluation of a Visualization Interface for Querying Large Unstructured Sound Databases. Abstract
Bencina, R., & Kaltenbrunner M. (2005).  The Design and Evolution of Fiducials for the reacTIVision System. Abstract
San Agustín, A. (2008).  Design and implementation of new communication channels between public and an artistic performance. Abstract
Casanovas, O. (2012).  Design of a nonlinear music playback system for nonprogrammer musicians. Abstract
Pons, J., & Serra X. (2017).  Designing Efficient Architectures for Modeling Temporal Features with Convolutional Neural Networks. 42th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017) . Abstract
Haro, G. G. (2008).  Detecting and Describing Percussive Events in Polyphonic Music. Abstract
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