For the Sharpmony team R&D is more than just a bunch of scientific and technical documents kept in drawers. We are convinced that the research we carry out in Artificial Intelligence and Parallel and Distributed Computing must be applied to achieve results that benefit society.
Sharpmony is the first application that makes use of more than 20 years of experience. If you are interested in our previous results of R&D applied to music, you may be interested in these papers we have published in the last years
Pacioni, E., Fernández De Vega, F.:
On the Impact of Directed Mutation Applied to Evolutionary 4-Part Harmony Models. Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer.
Francisco Fernández de Vega, J. Alvarado, A. Sánchez, M. Serrano, E. Pacioni:
Evolutionary Algorithms: A new hope for the future of music teaching. ACM GECCO (Companion) 2023: 65-66
F. Fernández de Vega:
"Enseñanzas profesionales y superiores de música asistidas por la Inteligencia artificial". Publicado en Educación, Investigación y Formación Musical: Miradas, Experiencias y Reflexiones desde los diferentes ámbitos y niveles Educativos, 2023, Dykinson. pp. 128-134.
R. Miragaia, F. Fernández de Vega, G. Reis, T. Inacio:
Evolving a Multi-classifier System for Multi-pitch Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programming. Applied Science, March 2021, 11(7).
R. Miragaia, F. Fernández de Vega, G. Reis:
Evolving a Multi-classifier System with Cartesian Genetic Programming for Multi-pitch Estimation of Piano Music. SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing, March 2021 Pages 472–480.
M. Morita, F. Fernández de Vega, J. Villegas:
Aplicacion de técnicas de aprendizaje profundo al reconocimiento optico de partituras SATB. CAEPIA 20/21, September 2021. Pages 411-416.
F. Fernández de Vega, J. Alvarado, M. Morita:
CAEPIA-App Competition: Sharpmony: A Computational Intelligence based tool for 4-part harmony. CAEPIA 20/21: Competition on mobile Apps with A.I. techniques. September 2021. Pages 1009-1012.
Francisco Fernández de Vega:
Revisiting the 4-part harmonization problem with GAs: A critical review and proposals for improving. CEC 2017: 1271-1278
Francisco Chávez de la O, Francisco Fernández de Vega, Francisco J. Rodriguez Diaz:
Analyzing quality clarinet sound using deep learning. A preliminary study. SSCI 2017: 1-7
Francisco Fernández de Vega, Carlos Cotta, Eduardo Reck Miranda:
Special issue on evolutionary music. Soft Comput. 16(12): 1995-1996 (2012)
Gustavo Reis, Francisco Fernández de Vega, Aníbal Ferreira:
Automatic Transcription of Polyphonic Piano Music Using Genetic Algorithms, Adaptive Spectral Envelope Modeling, and Dynamic Noise Level Estimation. IEEE Trans. Audio, Speech & Language Processing 20(8): 2313-2328 (2012)
G Reis, F Fernandéz, A Ferreira:
Evolutionary algorithms and automatic transcription of music. Proceedings of the 14th annual conference companion on Genetic and Evolutionary Computation Conference, ACM. pp 477-484. Best PhD student paper Award.
F Fernández, F Chávez, R Alcala, F Herrera:
Musical genre classification by means of Fuzzy Rule-Based Systems: A preliminary approach. Evolutionary Computation (CEC), 2011 IEEE Congress on, 2571-2577
G Reis, N Fonseca, F Fernandez, A Ferreira:
A genetic algorithm approach with harmonic structure evolution for polyphonic music transcription. Signal Processing and Information Technology, 2008. IEEE ISSPIT 2008.
Gustavo Reis, Nuno Fonseca, Francisco Fernández de Vega, Aníbal Ferreira:
Hybrid Genetic Algorithm Based on Gene Fragment Competition for Polyphonic Music Transcription. EvoWorkshops 2008: 305-314
Gustavo Reis, Francisco Fernández de Vega:
A novel approach to automatic music transcription using electronic synthesis and genetic algorithms. GECCO (Companion) 2007: 2915-2922