MIRtoolbox offers an integrated set of functions written in Matlab, dedicated to the extraction from audio files of musical features such as tonality, rhythm, structures, etc. The objective is to offer an overview of computational approaches in the area of Music Information Retrieval. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms. These building blocks form the basic vocabulary of the toolbox, which can then be freely articulated in new original ways. These elementary mechanisms integrates all the different variants proposed by alternative approaches - including new strategies we have developed -, that users can select and parametrize. This synthetic digest of feature extraction tools enables a capitalization of the originality offered by all the alternative strategies. Additionally to the basic computational processes, the toolbox also includes higher-level musical feature extraction tools, whose alternative strategies, and their multiple combinations, can be selected by the user.
The choice of an object-oriented design allows a large flexibility with respect to the syntax: the tools are combined in order to form a sets of methods that correspond to basic processes (spectrum, autocorrelation, frame decomposition, etc.) and musical features. These methods can adapt to a large area of objects as input. For instance, the autocorrelation method will behave differently with audio signal or envelope, and can adapt to frame decompositions.
The toolbox is conceived in the context of the Brain Tuning project financed by the European Union (FP6-NEST). One main objective is to investigate the relation between musical features and music-induced emotion and the associated neural activity.