Together with a team at Department of intelligent systems we started a machine learning-based approach to recognize different types of animal species based on the sound they produce. Currently supported species are Bumblebees, Birds, And Frogs.
A. Gradisek et al.: How to Recognize Animal Species Based On Sound – A Case Study On Bumblebees, Birds, And Frogs (IS 2015).
In our approach we used Mel-Frequency Cepstrum Coefficients (MFCC) as a feature vector alongside hundreds of others audio features. Data was preprocessed using Adobe Audition software. Features were extracted using openAUDIO feature extraction tool. Classification algorithms were created using WEKA open source machine learning software. The approach was tested on three groups of animals: bumblebees, with the largest number of samples (11 species, with queens and workers both represented in most cases, 20 classes in total), Slovenian frogs (13 species), and different species of cuckoos (7 species). The recordings of bumblebee were obtained in the field, frog sounds were obtained from the CD Frogs and toads of Slovenia  produced by Slovenian Wildlife Sound Archive , and the sounds of the cuckoos were obtained from the Chinese database 鸟类网.
In order to make the sound recognition application available to broader audiences, we have developed a web-based service where users can, apart from using only the species classification feature, upload their recordings to be later used in the learning set for further improvement of the classification. The application is now available at animal-sounds.ijs.si It runs in Slovenian, English, and Chinese.