Performance of Specific vs. Generic Feature Sets in Polyphonic Music Instrument Recognition

Abstract

Instrument identification in polyphonic audio recordings is a complex task which is beneficial for many music information retrieval applications. Due to the strong spectro-temporal differences between the sounds of existing instruments, different instrument-related features are required for building individual classification models. In our work we apply a multi-objective evolutionary feature selection paradigm to a large feature set minimizing both the classification error and the size of the used feature set. We compare two different feature selection methods. On the one hand we aim at building specific tradeoff feature sets which work best for the identification of a particular instrument. On the other hand we strive to design a generic feature set which on average performs comparably for all instrument classification tasks. The experiments show that the selected generic feature set approaches the performance of the selected instrument-specific feature sets, while a feature set specifically optimized for identifying a particular instrument yields degraded classification results if it is applied to other instruments.