Adaptive N-normalization for enhancing music similarity

The N-Normalization is an efficient method for normalizing a given similarity computed among multimedia objects. It can be considered for clustering and kernel enhancement. However, most approaches to N-Normalization parametrize the method arbitrarily in an ad-hoc manner. In this paper, we show that the optimal parameterization is tightly related to the geometry of the problem at hand. For that purpose, we propose a method for estimating an optimal parameterization given only the associated pair-wise similarities computed from any specific dataset. This allows us to normalize the similarity in a meaningful manner. More specifically, the proposed method allows us to improve retrieval performance as well as minimize unwanted phenomena such as hubs and orphans.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Mathieu Lagrange, George Tzanetakis
• Keywords: Associated Pair-wise Similarities, Efficient Method, ICASSP 2011, Optimal Parameterization, Signal Processing