Islands of Gaussians: The Self Organizing Map and Gaussian Music Similarity Features.
Sprache des Titels:
Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010)
Multivariate Gaussians are of special interest in the MIR
field of automatic music recommendation. They are used
as the de facto standard representation of music timbre to
compute music similarity. However, standard algorithms
for clustering and visualization are usually not designed
to handle Gaussian distributions and their attached metrics
(e.g. the Kullback-Leibler divergence). Hence to use these
features the algorithms generally handle them indirectly by
first mapping them to a vector space, for example by deriving
a feature vector representation from a similarity matrix.
This paper uses the symmetrized Kullback-Leibler centroid
of Gaussians to show how to avoid the vectorization
detour for the Self Organizing Maps (SOM) data visualization
algorithm. We propose an approach so that the algorithm
can directly and naturally work on Gaussian music
similarity features to compute maps of music collections.
We show that by using our approach we can create SOMs
which (1) better preserve the original similarity topology
and (2) are far less complex to compute, as the often costly
vectorization step is eliminated.