Mining Audio Data for Multiple Instrument Recognition in Classical Music

This paper addresses the problem of identification of multiple musical instruments in polyphonic recordings of classical music. A set of binary random forests was used as a classifier, and each random forest was trained to recognize the target class of sounds. Training data were prepared in two versions, one based on single sounds and their mixes, and the other based on sound frames taken from classical music recordings. The experiments on identification of multiple instrument sounds in recordings are presented, and their results are discussed in this paper.