Instrument Identification in Monophonic Music Using Spectral Information

Abstract

Various kinds of feature sets have been proposed to represent characteristics of musical instruments. While those feature sets have been chosen in a rather heuristic way, in this study, we demonstrate that the log-power spectrum suffices to represent characteristics that are essential to identifying instruments. For efficient encoding of instrument characteristics, we then reduce the number of features by applying the well-known dimension reduction techniques: principal component analysis (PCA) and linear discriminant analysis (LDA). For the classification of eight instruments, the features obtained by applying PCA-LDA to the log-power spectrum performed very well in comparison to existing methods with a recognition rate of 91% with as few as ten features.