Quality assessment of k-NN multi-label classification for music data

Abstract

This paper investigates problems related to quality assessment in the case of multi-label automatic classification of data, using kNearest Neighbor classifier. Various methods of assigning classes, as well as measures of assessing the quality of classification results are proposed and investigated both theoretically and in practical tests. In our experiments, audio data representing short music excerpts of various emotional contents were parameterized and then used for training and testing. Class labels represented emotions assigned to a given audio excerpt. The experiments show how various measures influence quality assessment of automatic classification of multi-label data. © Springer-Verlag Berlin Heidelberg 2006.