Similarity Search in Animal Sound Databases

In the past, similarity search for audio data has largely been focused on music. Recent digitization efforts in some of the larger animal sound archives bring other types of audio recordings into the focus of interest. Although recordings in animal sound archives are usually very well annotated by metadata, it is almost impossible to manually annotate all sounds made by animals in each recording. Complementary to classical text-based querying of databases that exploit available annotations, algorithms capable of automatically finding sections of recordings similar to a given query fragment provide a promising approach for content-based navigation. In our work, we present algorithms for feature extraction, as well as indexing and retrieval of animal sound recordings. Making use of a concept from image processing, the structure tensor, our feature extraction algorithm is adapted to the typical curve-like spectral features that are characteristic for many types of animal sounds. We propose a method for similarity search in animal sound databases which is obtained by adding a novel ranking scheme to an existing inverted file based approach for multimedia retrieval. Evaluation of our methods is based on recordings from the Animal Sound Archive, Berlin.