Sound and Music Description
Within this area of research we aim at automatically generating “descriptors” that capture the sonological or musical features that are embedded in the audio signals. By combining signal-processing techniques with machine learning approaches we have obtained good results in analyzing some of the basic and most important musical facets, such as rhythm (Gouyon, 2005; Zapata et al., 2012), timbre (Herrera et al., 2003), tonality (Gómez, 2006; Martorell and Gómez, 2011), melody (Salamon and Gómez, 2012), or structure (Ong, 2007). Then from this type of descriptions and by bringing in other methodological approaches we have been able to get into topics that are more related to the semantic description of music, such as the concept of complexity (Streich, 2007), similarity (Bogdanov et al., 2009), music recommendation (Celma, 2009), genre (Guaus, 2009), mood (Laurier et al., 2009), social tags (Sordo, 2011) or song covers (Serrà et al., 2009). In order to approach the semantic aspect of sound and music we are also carrying out research on music cognition modeling (Purwins et al., 2008a; Purwins et al., 2008b) and we are interested on the use of MIR technologies in different music traditions, specially in flamenco music.
Our team is also very active in the International Society of Music Information Retrieval (ISMIR) community. We have been involved in its scientific committee and we contribute to ethnocomp (interest group on computational ethnomusicology), WiMIR (Women in MIR) and we moderate some community projects: Teaching MIR and Audio Melody Extraction Annotation Inittiative.
- Emilia Gómez, co-leader
- Perfecto Herrera, co-leader
- Enric Guaus, postdoc
- Agustín Martorell, postdoc
- Julián Urbano, postdoc
- Oscar Mayor, research and development
- Juanjo Bosch, PhD student (PHENICX)
- Angel Faraldo, PhD student (GiantSteps)
- Martin Hermant, PhD student (GiantSteps)
- Nadine Kroher, PhD student (COFLA-SIGMUS)
- Cárthach O'Nuanain, PhD student (GiantSteps)
- Álvaro Sarasúa, PhD student (ESMUC, PHENICX)
We have contributed to several MTG's technologies
- Essentia: our inhouse technology for semantic analysis of sound and music, used in diffferent application contexts.
- Downloadable software:
- Downloadable datasets:
We are currently working on the following projects:
- PHENICX: Performances as Highly Enriched aNd Interactive Concert eXperiences, in collaboration with the Audio Signal Processing and Musical and Advanced Interaction areas of the MTG.
- GiantSteps: Seven League Boots for Music Creation and Performance, in collaboration with the Musical and Advanced Interaction Area of the MTG.
- SIGMUS: Signal Analysis for the Discovery of Traditional Music Repertories
COFLA: Computational analysis of flamenco music