Analytic Comparison of Audio Feature Sets using Self-Organising Maps

R. Mayer, J. Frank, A. Rauber:
"Analytic Comparison of Audio Feature Sets using Self-Organising Maps";
Vortrag: WEMIS 2009 - Workshop on Exploring Musical Information Spaces, Korfu; 01.10.2009 - 02.10.2009; in:"WEMIS 2009 - Workshop on Exploring Musical Information Spaces", University of Alicante, Alicante, Spain (2009), ISBN: 978-84-692-6082-1; S. 62 - 67.

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Abstract:


A wealth of different feature sets for analysing music
has been proposed and employed in several different Music Information
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.