Analytic Comparison of Audio Feature Sets using Self-Organising Maps

A wealth of different feature sets for analysing music has been proposed and employed in several different Music Infor- mation Retrieval applications. In many cases, the feature sets are compared with each other based on benchmarks in supervised machine learning, such as automatic genre classification. While this approach makes features comparable for specific tasks, it doesn't reveal much detail on the specific musical characteristics captured by the single feature sets. In this paper, we thus perform an analytic comparison of several different audio feature sets by means of Self-Organising Maps. They perform a projection from a high dimensional input space (the audio features) to a lower dimensional output space, often a two-dimensional map, while preserving the topological order of the input space. Comparing the stability of this projection allows to draw conclusions on the specific properties of the single feature sets.