NEW: I obtained a funding from the Research Council of Norway under the FRIPRO-IKTPLUSS program:
MIRAGE: A Comprehensive AI-Based System for Advanced Music Analysis
One main goal is to greatly improve the machine’s capability to listen to and understand music. This will lead to groundbreaking technologies to help everybody better understand and appreciate music. One main application is to make music more accessible and engaging.
We extend further the design of our leading computational framework aimed at extracting a large set of information from music, such as timbre, notes, rhythm, tonality or structure. Yet music can easily become complex. To make sense of such a subtle language, refined musicological considerations need to be formalised and integrated into the framework. Music is a lot about repetition: motives are repeated many times within a piece, and pieces of music imitate each other and cluster into styles. Revealing this repetition is both challenging and crucial. A large range of musical styles will be considered: traditional, classical and popular; acoustic and electronic; and from various cultures. The rich description of music provided by this new computer tool will also be used to investigate elaborate notions such as emotions, groove or mental images.
Beyond the academic domains of musicology, music informatics and music cognition, this project is oriented towards the development of groundbreaking technologies for the general public. Music videos have the potential to significantly increase music appreciation. The effect is increased when music and video are closely articulated. Our technologies will enable to generate videos on the fly for any music. One challenge in music listening is that it all depends on the listeners’ implicit ear training. Automated, immersive, interactive visualisations will help listeners (even hearing-impaired) understand and appreciate better the music they like (or don’t like yet). This will make music more accessible and engaging. It will be also possible to visually browse into large music catalogues. Applications to music therapy will also be considered.
Short bio: Olivier Lartillot is a researcher working in the fields of computational music and sound analysis and artificial intelligence. He designed MIRtoolbox, a referential tool for music feature extraction from audio. He also works on symbolic music analysis, notably on sequential pattern mining. In the context of his 5-year Academy of Finland research fellowship, he conceived the MiningSuite, an analytical framework that combines audio and symbolic research.
Olivier just finished working for the SoundTracer project, an innovation project in collaboration with the National Library of Norway. The idea was to develop a prototype mobile app for a "query-by-gesturing" system, in which the user would perform a gesture and retrieve a sound file from the entire collection of the National Library of Norway.
As part of the SoundTracer project, he worked in particular on the automated transcription of Norwegian fiddle music. He discussed this topic in the international conference on music perception and cognition (ICMPC 2018):
Olivier continues developing his music analysis software MiningSuite with planned integration of musical gesture analysis. He also collaborates on the TIME project.
Olivier has given courses in Music Information Retrieval, conference tutorials and has taught in various summer schools. He has collaborated on various projects around the topics of artificial intelligence, signal processing, cognitive science, neuroscience, music analysis, ethnomusicology and music therapy. He has written 80 articles, with more than 2000 citations. He is a member of the Editorial Board of the Transactions of the International Society for Music Information Retrieval, is an expert evaluator for the European Commission's Horizon 2020 program and has participated to the Program Committees of various conferences.
- Researcher at the Department for Architecture, Design and Media Technology, Aalborg University, Denmark (2014-2016)
- Academy of Finland research fellow (2009-2014) and previously post-doc (2004-2009) at the Finnish Centre of Excellence in Interdisciplinary Music Research, University of Jyväskylä, Finland
- Scientific collaborator at the Swiss Center for Affective Sciences, University of Geneva, Switzerland (2012-2013)
- PhD at Ircam - Centre George Pompidou, UPMC Paris University (2000-2004)
- ATIAM Master at Ircam - Centre George Pompidou, UPMC Paris University (1999-2000)
- Supélec engineering school, France (1996-1999)
- Art degree in Musicology, Sorbonne University, Paris, France (1996-1999)
Ved å bevege mobiltelefonen i lufta kan du søke etter musikk på biblioteket. En ny app gjør det mulig.
Foto: Toril Haugen
- Lartillot, Olivier; Thedens, Hans-Hinrich & Jensenius, Alexander Refsum (2018). Computational model of pitch detection, perceptive foundations, and application to Norwegian fiddle music, In Richard Parncutt & Sabrina Sattmann (ed.), Proceedings of ICMPC15/ESCOM10. Centre for Systematic Musicology, University of Graz. ISBN 978-3-200-05771-5. article. s 252 - 255 Show summary
- Lartillot, Olivier (2018). Analyse musicologique par ordinateur d'enregistrements audio et de partitions informatisées. Show summary
- Lartillot, Olivier (2018). Computational musicological analysis of notated music: a brief overview. Show summary
- Lartillot, Olivier (2018). MIRToolbox and MiningSuite.
- Lartillot, Olivier (2018). Music and Voice Analysis using Matlab: MIRtoolbox and the MiningSuite.
- Lartillot, Olivier (2018). The MiningSuite - a Matlab toolbox for signal, audio and music analysis. Show summary
- Lartillot, Olivier (2018). mirtempo 1.8: Tempo Estimation By Tracking a Complete Metrical Structure. Show summary