Our research on Machine Learning and Case-Based Reasoning systems has been applied to model different creative musical processes:

  • We are currently focused on understanding the expressive resources used by guitar players. We are interested in analyzing and modeling the use of these expressive resources considering the musical structure of a piece, its musical genre, and the personal traits of the players. Visit our website GuitarLab for a more detailed explanation of our project.
  • We have studied the musical expressivity on professional violin performers
  • The PhD research of Claudio Baccigalupo is devoted on the desing of a Social Web Radio.
  • The projects TABASCO and CBR-ProMusic have
    studied the issue of expressiveness in computer generated music.

Visit our Machine Learning for Music web page.

Content-based Audio Transformation

Initial/final date: 
28 December 2000 to 27 December 2003
Main researcher: 
Project type: 
-
Funding Entity: 
TIC 2000-1094-C02
Description: 
This project will study and develop a support tool for musical recording manipulation with the goal of enhancing sound and musical qualities in professional sound post production. Specifically, the project will address the description of musical attributes, music database storage and retrieval of those descriptions, and sound transformation based on descriptions using physical attributes (e.g. pitch), perceptual attributes (e.g. brightness), and musical knowledge (e.g. tension). The components used for sound content analysis and description, as well as the components for physical manipulation of sound, are based on spectral modelling analysis and synthesis techniques. Moreover, the descriptions to be used will be compatible as far as possible with the future MPEG-7 standard for multimedia content description. The transformation components will use both case-based reasoning techniques and modelling of musical knowledge.
Funding Amount (€): 
0.00
Research line: 
Machine Learning for Music
Acronym: 
TABASCO

Case-Based Reasoning for Content-Based Music Processing

Initial/final date: 
01 December 2003 to 30 November 2006
Main researcher: 
Project type: 
-
Funding Entity: 
TIC2003-07776-C02-02
Description: 
The aim of the project is to work on different aspects of content-based music processing. The project will study and develop tools for musical content extraction, modeling, and processing. Specifically, we will investigate the use of artificial intelligence techniques, such as case-based reasoning, for content-based melody processing.
Funding Amount (€): 
0.00
Research line: 
Machine Learning for Music
Acronym: 
CBR-ProMusic