Biblio

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B
Barbosa, A., Cardoso J., & Geiger G. (2005).  Network Latency Adaptive Tempo in the Public Sound Objects System. Abstract
Blaauw, M., & Bonada J. (2017).  A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs. Applied Sciences. 7(12),  Abstract
Blaauw, M., & Bonada J. (2017).  A Neural Parametric Singing Synthesizer. 18th Annual Conference of the International Speech Communication Association (Interspeech). 1230-1234.
Blanco, A. D., & Ramirez R. (2017).  Neural correlates of bow learning technique. Proceedings of the 10th International Workshop on Machine Learning and Music. 1-6. Abstract
Bucci, A., Pérez A., & Bonada J. (2012).  Non-Impulsive Signal Deconvolution for Computation of Violin Impulse Responses. IEEE International Conference on Acoustics, Speech, and Signal Processing. Abstract
application/pdf iconDeconvolution.pdf
C
Cano, P., Koppenberger M., Le Groux S., Ricard J., Wack N., & Herrera P. (2005).  Nearest-Neighbor Automatic Sound Classification with a WordNet Taxonomy. Journal of Intelligent Information Systems. 24, 99-111. Abstract
Cano, P., Koppenberger M., Le Groux S., Ricard J., Herrera P., & Wack N. (2004).  Nearest-neighbor generic sound classification with a wordnet-based taxonomy. Abstract
Carabias-Orti, J. J., Cobos M., Vera-Candeas P., & Rodríguez-Serrano F. J. (2013).  Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings. EURASIP Journal on Advances in Signal Processing. Abstract
Caro Repetto, R., & Serra X. (2016).  NACTA: construyendo el futuro de la tradición del jingju. Itinerarios, Espacios, Contextos: XIV congreso de la Sociedad de Etnomusicología, IX congreso de IASPM España. Abstract
application/pdf iconCaro & Serra - NACTA y jingju - SIBE 2017 (presentation slides)
Celma, Ò., & Herrera P. (2008).  A New Approach to Evaluating Novel Recommendations. 2008 ACM Conference on Recommender Systems. Abstract
application/pdf iconpostprint version
Chen, G., Wang T. - G., & Herrera P. (2008).  A Novel Music Retrieval System with Relevance Feedback. Third International Conference on Innovative Computing, Information and Control. Abstract
D
Delgado, N. (2017).  Neural Correlates of Music and Emotion in ASD and Typical Children. Abstract
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G. Mahedero, J., Cano P., Martinez A., Gouyon F., & Koppenberger M. (2005).  Natural language processing of lyrics. Abstract
G. Risueño, M. (2008).  NAUTILUS. A Musical, Collaborative and Interactive Sonification Toolkit. Abstract
Guaus, E. (2004).  New approaches for rhythmic description of audio signals. Abstract
Guaus, E., & Batlle E. (2004).  A non-linear rhythm-based style classifcation for Broadcast Speech-Music Discrimination. 116th AES Convention. Abstract
J
Jensenius, A., Camurri A., Castagné N., Maestre E., Malloch J., McGilvray D., et al. (2007).  The need of formats for streaming and storing music-related movement and gesture data. Abstract
Jordà, S. (2001).  New Musical Interfaces and New Music-making Paradigms. Abstract
M
Musevic, S. (2009).  Non-stationary sinusoidal analysis. Abstract
Musevic, S. (2013).  Non-Stationary Sinusoidal Analysis. Department of Information and Communication Technologies. 210. Abstract
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Oramas, S., Espinosa-Anke L., Gómez F., & Serra X. (2018).  Natural Language Processing for Music Knowledge Discovery. Journal of New Music Research. Abstract
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Patlatzoglou, K. (2016).  Neural and Music Correlates of Music-evoked Emotions. Abstract
Pretto, N., Bozkurt B., Caro Repetto R., & Serra X. (2018).  Nawba Recognition for Arab-Andalusian Music Using Templates From Music Scores. Sound and Music Computing Conference. Abstract
Purwins, H., Blankertz B., & Obermayer K. (2000).  A new method for tracking modulations in tonal music in audio data format. International Joint Conference on Neural Network (IJCNN'00). 6, 270-275. Abstract
R
Romaní Picas, O. (2014).  A novel audio-to-score alignment method using velocity-driven DTW. Abstract
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