MUSCLE eTeam: 

Semantic from Audio and Genre Classification for Music 

(MUSCLE NoE | All e-teams)


This eTeam will collaborate on different methods for audio feature extraction and their appliance in both supervized classification as well as unsuperviced organization as a means to access and explore audio holdings such as sound archives or, particularly, music. The eTeam partners have strong expertise on extracting descriptors from audio data, specialized on music, instruments and other sounds. Moreover, there is also expertise on text mining and therefore textual genre analysis will be combined with the audio-based approaches. Furthermore, the partners have core competencies in the application of machine learning techniques for the analysis and structuring of content, and subsequent visualization in 2D as well as 3D environments.
The eTeam will be a concentrated effort on:

Furthermore, the feature sets evaluated in the classification activities will be employed in unsupervized machine learning tasks in order to provide an automatic clustering of audio archives, which in turn serves as an interface for browsing and exploration. eTeam partners will bring in expertise on visualization, providing intuitive interfaces for both 2D visualizations as well as interactive 3D environments for future access models to audio archives.

Results of the eTeam will be:


Contribution of partners

Previous activities

Tentative plan of activities

The eTeam fosters collaborations between the participants and the exchange of know-how in the different domains and expertises described.
eTeam activites will be supported by exchange of researchers between the eTeam institutions as well as writing joint publications (conference papers, articles).


Andreas Rauber
Dept. of Software Technology and Interactive Systems
Vienna Univ. of Technology
Favoritenstr. 9 - 11 / 188
A - 1040 Wien
AUSTRIA e-mail: rauber@ifs.tuwien.ac.at