Clustering Driven Cascade Classifiers for Multi-indexing of Polyphonic Music by Instruments

Abstract

Recognition and separation of sounds played by various instruments is very useful in labeling audio files with semantic information. Numerous approaches on acoustic feature extraction have already been proposed for timbre recognition. Unfortunately, none of these monophonic timbre estimation algorithms can be successfully applied to polyphonic sounds, which are more usual cases in the real music world. This has stimulated the research on a hierarchically structured cascade classification system under the inspiration of the human perceptual process. This cascade classification system makes first estimate on the higher level of the decision attribute, which stands for the musical instrument family. Then, the further estimation is done within that specific family range. However, the traditional hierarchical structures were constructed in human semantics, which are meaningful from human perspective but not appropriate for the cascade system. We introduce the new hierarchical instrument schema according to the clustering results of the acoustic features. This new schema better describes the similarity among different instruments or among different playing techniques of the same instrument. The classification results show a higher accuracy of cascade system with the new schema compared to the traditional schemas.