An Empirical Study of Feature Extraction Methods for Audio Classification

Abstract

With the growing popularity of video sharing web sites and the increasing use of consumer-level video capture devices, new algorithms are needed for intelligent searching and indexing of such data. The audio from these video streams is particularly challenging due to its low quality and high variability. Here, we perform a broad empirical study of features used for intelligent audio processing. We perform experiments on a dataset of 200 consumer videos over which we attempt to detect 10 semantic audio concepts.