Musical Instrument Identification in Continuous Recordings
Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (bassoon, clarinet, flute, guitar, piano, cello and violin), which yields a high recognition rate. A large and very diverse solo database (108 different solos, all by different performers) is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. First we bring classification results using a very extensive collection of features (62 different feature types), and then use our GDE feature selection algorithm to select a smaller feature set with a relatively short computation time, which allows us to perform instrument recognition in solos in real-time, with only a slight decrease in recognition rate. We demonstrate that our real-time solo classifier can also be useful for instrument recognition in duet performances, and improved using simple “source reduction”.