The Music Informatics Group is a specialized research group within the Department of Computer Science and Artificial Intelligence at the University of the Basque Country UPV/EHU, Gipuzkoa Campus, San Sebastián.

Music Informatics is the study of computational models of music analysis, music generation, and music information retrieval. The group is interested in statistical modelling, computational musicology, music knowledge representation, and pattern discovery, from the perspectives of theoretical foundation through to algorithms and implementation.

current
• Darrell Conklin (group leader)
• Victor Padilla (Universidad Internacional de la Rioja, Madrid)
• Kerstin Neubarth (Europa-Universität Flensburg)
• Jorge Langa (doctoral student, UPV/EHU)

former staff
• Izaro Goienetxea (postgraduate researcher)
• Ray Whorley (postdoctoral researcher)
• Thomas Rocher (postdoctoral researcher)
• Louis Bigo (postdoctoral researcher)
• Elsa Fernandez (postdoctoral researcher)

guest researchers and visitors
• Tetsuro Kitahara (Nihon University)
• Eita Nakamura (postdoctoral researcher)
• Paco Gómez (Universidad Politécnica de Madrid)
• Joaquin Mora (Universidad de Sevilla)
• Dorien Herremans (postgraduate researcher)
• Tillman Weyde (City, University of London)
• David Rizo (University of Alicante)
• Ryan Groves (postgraduate researcher)
• Olivier Lartillot (postdoctoral researcher)
• Sertan Senturk (postgraduate researcher)
• Peter van Kranenburg (postdoctoral researcher)
• Ruben Hillewaere (postgraduate researcher)
• Varun deCastro-Arrazola (postgraduate researcher)

Machine Learning and Music

• Descriptive models. algorithms for pattern representation and discovery in music, currently with applications to folk music corpora and music analysis.
• Music generation. If we have a good statistical model of a composer, performer, genre, etc., how can we use that model in an "inverted" manner to generate music? This broad theme brings up profound issues in machine learning, statistical model architectures, musical coherence, music knowledge representation, and optimization and sampling.
• Knowledge representation. Continued development of the viewpoints method, based on algebraic data types and description logics. Research into semiotic patterns which can capture long range dependencies in music. Maintainer and developer of the MIDI-Perl library.

Bioinformatics and genomics

• Analysis of next generation sequencing data, genome assembly, intron/exon boundary detection, SNP discovery in the European anchovy, the Atlantic mackerel, and the tench. Collaboration with the Genomic Resources research group, Faculty of Science and Technology, University of the Basque Country.