Biblio

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Gómez, E., Grachten M., Hanjalic A., Janer J., Jordà S., Julià C. F., et al. (2013).  PHENICX: Performances as Highly Enriched aNd Interactive Concert Experiences. SMAC Stockholm Music Acoustics Conference 2013, SMC Sound and Music Computing Conference 2013.
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.
Gómez, E. (2011).  Integrating Different Knowledge Sources for the Computational Modeling of Flamenco Music. (Müller, M., Goto, M., Dixon, S., Ed.).Multimodal Music Processing (Dagstuhl Seminar 11041) - Dagstuhl Reports. 1(1), 82-83. Abstract
Gómez, E., Bonada J., & Salamon J. (2012).  Automatic Transcription of Flamenco Singing from Monophonic and Polyphonic Music Recordings. III Interdisciplinary Conference on Flamenco Research (INFLA) and II International Workshop of Folk Music Analysis (FMA). Abstract
Gómez, E. (2006).  Tonal description of polyphonic audio for music content processing. INFORMS Journal on Computing, Special Cluster on Computation in Music. 18, Abstract
Gómez, E., Schedl M., Serra X., & Hu X. (2016).  Music Information Retrieval:
 Overview, Recent Developments and Future Challenges. 17th International Society for Music Information Retrieval (ISMIR) Conference. 11-12. Abstract
Gómez, E. (2006).  Tonal Description of Music Audio Signals. Department of Information and Communication Technologies. Abstract
Gómez, E., & Bonada J. (2008).  Automatic Melodic Transcription of Flamenco Singing. Fourth Conference on Interdisciplinary Musicology (CIM08). Abstract
Gómez, P., Pikrakis A., Mora J., Díaz-Báñez J. M., Gómez E., Escobar Borrego F., et al. (2012).  Automatic Detection of Melodic Patterns in Flamenco Singing by Analyzing Polyphonic Music Recordings. III Interdisciplinary Conference on Flamenco Research (INFLA) and II International Workshop of Folk Music Analysis (FMA). Abstract
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