The automatic ontology generation system performs two main tasks: i) musical instrument recognition, and ii) the construction of instrument concept hierarchies. In the first part, the hybrid system uses either a Multi-Layer Perceptron neural network, or Support Vector Machines to model the relationships between instruments (e.g., violin) and their attributes (e.g. bowed) using content-based timbre features. In the second part, the output of the instrument recognition system is processed using Formal Concept Analysis to construct a conceptual hierarchy for musical instruments.
The system is based on a general conceptual analysis approach and can be applied to any research fields that deal with knowledge management issues. For more details regarding the generated OWL files published in , see this link. For the complete OWL files generated in the experiment published in , see this link.
 Sefki Kolozali, Mathieu Barthet, George Fazekas, Mark Sandler. Automatic Ontology Generation for Musical Instruments based on Audio Analysis. IEEE Transactions on Audio, Speech, and Language Processing, 2013.
 Sefki Kolozali, George Fazekas, Mathieu Barthet, Mark Sandler. A framework for automatic ontology generation based on semantic audio analysis. In proceedings of the 53rd International Conference of the Audio Engineering Society on Semantic Audio, 2014.