Towards an Optimal Feature Set for Environmental Sound Recognition

Feature selection for audio retrieval is a non-trivial task. In this paper we aim at identifying an optimal feature combination for environmental sound recognition. The feature combination is constructed from a broad set of features. Additionally to state-of-the-art features, we evaluate the quality of audio features we previously introduced for another domain. We examine the properties of features by quantitative data analysis (factor analysis) and identify candidates for feature combinations. We verify the quality of the combination by retrieval experiments. The optimal solution yields Recall and Precision values of 87% and 88%, respectively.