Biblio

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Z
Zanin, M., Buldú J. M., Cano P., & Boccaletti S. (2008).  Disorder and decision cost in spatial networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 18(2),  Abstract
Y
Yesiler, F., Tralie C., Correya A., Silva D. F., Tovstogan P., Gómez E., et al. (2019).  Da-TACOS: A dataset for cover song identification and understanding. International Society for Music Information Retrieval (ISMIR). Abstract
W
Wüst, O. (2003).  Database-Driven Systems and Applications for Music Information Management. Abstract
V
Vinyes, M., Bonada J., & Loscos A. (2006).  Demixing Commercial Music Productions via Human-Assisted Time-Frequency Masking. Abstract
Vamvakousis, Z. (2016).  Digital Musical Instruments for People with Physical Disabilities. Department of Information and Communication Technologies. 163.
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T
Tutzer, F. (2011).  Drum rhythm retrieval based on rhythm- and sound similarity. Abstract
application/pdf iconTutzer-Felix-Master-thesis-2011.pdf
Tizón, M., Gómez F., & Oramas S. (2014).  Does Always the Phrygian Mode Elicit Responses of Negative Valence?. 4th International Workshop on Folk Music Analysis (FMA). Abstract
application/pdf iconEmotions-FMA2014.pdf
S
Streich, S., & Herrera P. (2005).  Detrended Fluctuation Analysis of Music Signals Danceability Estimation and further Semantic Characterization. AES 118th Convention. 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
Sola, J. (1997).  Disseny i implementació d`un sintetitzador de piano. 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
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
San Agustín, A. (2008).  Design and implementation of new communication channels between public and an artistic performance. Abstract
Salselas, I., Hazan A., Herrera P., & Purwins H. (2008).  The Development of Melodic Representations at Early Age: Towards a Computational Model.. Music, Language and the Mind 2008. Abstract
Salselas, I. (2007).  The Development of Melodic Representations at Early Age Facts Towards a Computational Model. Abstract
Salselas, I., & Herrera P. (2010).  Development of Perception and Representation of Rhythmic Information: Towards a Computational Model. 2010 IEEE 9th International Conference on Development and Learning. Abstract
application/pdf iconDevelopment of Perception and Representation of Rhythmic Information.pdf
R
Román Echeverri, C. G. (2011).  Detection of genre-specific musical instruments: The case of the mellotron. Abstract
application/pdf iconRoman-Carlos-Master-thesis-2011.pdf
Ramirez, R., Hazan A., Gómez E., Maestre E., & Serra X. (2005).  Discovering Expressive Transformation Rules from Saxophone Jazz Performances. Journal of New Music Research. 34, 319-330. Abstract
Ramires, A., & Serra X. (2019).  Data augmentation for instrument classification robust to audio effects. 22nd International Conference on Digital Audio Effects (DAFx-19). Abstract
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Porter, A., Sordo M., & Serra X. (2013).  Dunya: A System for Browsing Audio Music Collections Exploiting Cultural Context. 14th International Society for Music Information Retrieval Conference (ISMIR 2013). 101-106. Abstract
Pons, J. (2019).  Deep Neural Networks for Music and Audio Tagging. Information and Communication Technologies. 216+xxiii. 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
Parera, J. (2016).  DJ Codo Nudo: a novel method for seamless transition between songs for electronic music. Abstract
application/pdf iconJaume-Parera-Master-thesis-2016.pdf
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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
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Nogueira, W., Gajęcki T., Krüger B., Janer J., & Büchner A. (2016).  Development of a Sound Coding Strategy based on a Deep Recurrent Neural Network for Monaural Source Separation in Cochlear Implants. 12th ITG conference on Speech Communication. Abstract
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