Analytical Features: A Knowledge-Based Approach to Audio Feature Generation
- PDF / 1,252,789 Bytes
- 23 Pages / 600.05 x 792 pts Page_size
- 59 Downloads / 197 Views
Research Article Analytical Features: A Knowledge-Based Approach to Audio Feature Generation Franc¸ois Pachet and Pierre Roy Sony CSL-Paris, 6, rue Amyot, 75005 Paris, France Correspondence should be addressed to Franc¸ois Pachet, [email protected] Received 4 September 2008; Accepted 16 January 2009 Recommended by Richard Heusdens We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features (AFs), a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs, in particular their dimensional type system, on which are based pattern-based random generators, heuristics, and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks, demonstrating their advantage over standard audio features. More generally, we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation. Copyright © 2009 F. Pachet and P. Roy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction This paper addresses two fundamental questions of human perception: (1) to what extent are human perceptual categorization for items based on objective features of these items, and (2) in these situations, can we identify these objective features explicitly? A natural paradigm for addressing these questions is supervised classification. Given a data set with perceptive labels considered as ground truth, the question becomes how to train classifiers on this ground truth so that they can generalize and classify new items correctly, that is, as humans would? A crucial ingredient in supervised classification is the feature set that describes the items to be classified. In machine-learning research, it is typically assumed that features naturally arise from the problem definition. However, good feature sets may not be directly available, motivating the need for techniques to generate features from the raw representations of objects, signals in particular. In this paper, we claim that the generation of good feature sets for signal-based classification requires the representation of various types of knowledge about signal processing. We propose a framework to demonstrate and accomplish this process.
1.1. Concept Induction from Symbolic Data. The idea of automatically deriving features that describe objects or situations probably originated from Samuel’s pioneering work on a program that played the game of checkers [1]. In order to evaluate a position, the program needed a number of features describing the important properties of the board [2]. These features were designed by hand, and Samuel considered the automatic c
Data Loading...
