Exploring Billions of Audio Features

Many works in audio and image signal analysis are based on the use of "features" to represent characteristics of sounds or images. Features are used in various ways, for instance as inputs to classifiers to categorize automatically objects, e.g. for audio scene description. Most, if not all, approaches focus on the development of clever classifiers and on the various processes of feature selection, classifier algorithms and parameter tuning. Strangely, the features themselves are rarely justified. The predominant paradigm consists in selecting, by hand, "generic", well-known features from the literature and focusing on the rest of the chain. In this study we try to generalize the notion of feature to make the choice of features more systematic and less prone to hazardous, unjustified human choices. We introduce to this aim the notion of "analytical feature": features built only from the analysis of the problem at hand, using a heuristic function generation process. We show some experiments aiming at answering some general questions about analytical features.