Audio recognition in the wild: Static and dynamic classification on a real-world database of animal vocalizations

Abstract

We present a study on purely data-based recognition of animal sounds, performing evaluation on a real-world database obtained from the Humboldt-University Animal Sound Archive. As we avoid a preselection of friendly cases, the challenge for the classifiers is to discriminate between species regardless of the age or stance of the animal. We define classification tasks that can be useful for information retrieval and indexing, facilitating categorization of large sound archives. On these tasks, we compare dynamic and static classification by left-right and cyclic Hidden Markov Models, recurrent neural networks with Long Short-Term Memory, and Support Vector Machines, as well as different features commonly found in sound classification and speech recognition, achieving up to 81.3 % accuracy on a 2-class, and 64.0 % on a 5-class task.