A multiple-expert framework for instrument recognition

Instrument recognition is an important task in music information retrieval (MIR). Whereas the recognition of musical instruments in monophonic recordings has been studied widely, the polyphonic case still is far from being solved. A new approach towards feature-based instrument recognition is presented that makes use of redundancies in the harmonic structure and temporal development of a note. The structure of the proposed method is targeted at transferability towards use on polyphonic material. Multiple feature categories are extracted and classified separately with SVM models. In a further step, class probabilities are aggregated in a two-step combination scheme. The presented system was evaluated on a dataset of 3300 isolated single notes. Different aggregation methods are compared. As the results of the joined classification outperform individual categories, further development of the presented technique is motivated.