Ircammusicgenre and Ircammusicmood softwares estimate automatically the belonging of a music track to a set of music genre (electronica, jazz, pop/rock…) and music mood classes (positive, sad, powerful, calming…)
Classification of music items are generally primarily based on their belonging to a music genre: electronica, jazz, pop/ rock… However, the editorial meta-data related
to the genre are generally only accessible at the artist level (the
whole set of music tracks produced by one artist will belong to the same
music genre whatever the tracks content). Ircammusicgenre is a software which allows the automatic estimation of the belonging of a music track to music genres. The list of music genres considered by the software can be
pre-determined by Ircam (electronica, jazz, pop/rock…) or can be adapted
to categories relevant to the partner, provided a sufficient number of
sound examples per category.
Ircammusicgenre also allows to perform multi-labeling of a music track,
i.e. assigning a set of genre labels instead of a single genre. In this case, a weighting is assigned to each estimated label.
Ircammusicmood a software which allows the automatic estimate of the music mood of has music track to music mood. Music mood report to the “mood” that a track suggests: positive, sad, powerful, calming…
As for the music genre, the list can be predetermined by Ircam or discussed with the partner. Multi-labels can also be applied to the music mood classification.
Ircammusicgenre and Ircammusicmood are based on the Ircamclassifier technology.
Ircamclassifier allows to learn new concepts related to music contents by training on example databases. For this, a large set of audio features are extracted from labeled music items and are used to find relationships between the labels and the example audio contents. Ircamclassifier uses over 500 different audio features, performs automatic feature selection and statistical model parameter selection.
Ircamclassifier uses a full-binarization process of the labels and a set of SVM classifiers. Mono-labeling and multi-labeling are obtained from the set of SVM decisions. Performances and computation time of the resulting trained system are then optimized for a specific tasks given a ready-to-use system for music-genre or musicmood.
Ircammusicgenre and Ircammusicmood are available as software or as a dynamic library for Windows, Mac OS-X and Linux platform.