1 research outputs found
Is There a Relation Between the Syntax and the Fitness of an Audio Feature?
Feature generation has been proposed recently to generate
feature sets automatically, as opposed to human-designed
feature sets. This technique has shown promising results
in many areas of supervised classification, in particular in
the audio domain. However, feature generation is usually
performed blindly, with genetic algorithms. As a result
search performance is poor, thereby limiting its practical
use. We propose a method to increase the search performance
of feature generation systems. We focus on analytical
features, i.e. features determined by their syntax.
Our method consists in first extracting statistical properties
of the feature space called spin patterns, by analogy
with statistical physics. We show that spin patterns carry
information about the topology of the feature space. We
exploit these spin patterns to guide a simulated annealing
algorithm specifically designed for feature generation. We
evaluate our approach on three audio classification problems,
and show that it increases performance by an order
of magnitude. More generally this work is a first step in
using tools from statistical physics for the supervised classification
of complex audio signals