Evolutionary feature synthesis for content-based audio retrieval

Although there is a wide variety of low-level audio features for content-based audio indexing and retrieval, they may lack the discrimination power needed for accurate description of the aural content, leading into a poor content-based retrieval performance. Furthermore, manual selection of features among a vast collection may easily lead into sub-optimal solutions. In this paper, we propose an evolutionary feature synthesis technique, which co-exists with a feature selection scheme. The synthesis process seeks for the optimal linear / non-linear operators and feature weights from a pre-defined search space, so as to synthesize a highly discriminative set of new (artificial) features from the set of selected features. The evolutionary search process in the multi-dimensional solution space is based on multi-dimensional particle swarm optimization (MD PSO) algorithm, along with a fractional global best formation (FGBF) technique. Unlike in many existing feature generation approaches found in the literature, the dimension of the synthesized feature vector is also optimized during the process. The synthesized features by the proposed approach are compared with original audio descriptors in an extensive set of retrieval tasks. The experimental results clearly demonstrate a crucial improvement of up to 15-25% in the retrieval performance. Moreover, the proposed synthesis technique surpasses the performance of the artificial neural networks for retrieving accurate audio content.