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.
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. (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
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
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
P
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., & 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
Pons, J. (2019).  Deep Neural Networks for Music and Audio Tagging. Information and Communication Technologies. 216+xxiii. 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
O
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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