Investigating combinations of visual audio features and distance metrics in the problem of audio classification

Abstract

The article addresses a problem of audio signal classification employing image processing and recognition methods. In such an approach, vectorized audio signal features are converted into a matrix representation (feature map), and then processed, as a regular image. In the paper, we present a process of creating a low-dimensional feature space by means of two-dimensional Linear Discriminant Analysis and projecting input featuremaps into this subspace using two-dimensional Karhunen–Loeve Transform. The classification is performed in the reduced feature space by means of voting on selected distance metrics applied for various features. The experiments were aimed at finding an optimal (in terms of classification accuracy) combination of six feature types and five distance metrics.The found combination makes it possible to perform audio classification with high accuracy, yet the dimensionality of resulting feature space is significantly lower than input data.