On the robustness of audio features for musical instrument classification

We examine the robustness of several audio features applied exemplarily to musical instrument classification. For this purpose we study the robustness of 15 MPEG-7 Audio Low-Level Descriptors and 13 further spectral, temporal, and perceptual features against four types of signal modifications: low-pass filtering, coding artifacts, white noise, and reverberation. The robustness of the 120 feature coefficients obtained is evaluated with three different methods: comparison of rankings obtained by feature selection techniques, qualitative evaluation of changes in statistical parameters, and classification experiments using Gaussian Mixture Models (GMMs). These experiments are performed on isolated notes of 14 musical instrument classes.