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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.

Event recognition systems have high potential to support crisis management and emergency response. For large-scale scenarios, however, the sheer amount of possible audio and video channels requires adequate processing of the material by automatic means. In this article, the authors focus on automatic audio and video event recognition, by means of detecting abnormalities both in train noise as well as surveillance videos, and by conducting automatic speech recognition on fire fighter communication. All components are integrated in an overall intelligent resource management system. The authors elaborate on the challenges expected from real life data and the solutions that the authors applied. The overall system, based on Event-Driven Service-Oriented Architecture, has been implemented and partly integrated into the end users' infrastructures. The system has been continuously running for more than two years, collecting data for research purposes.

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