Automatic Identification of Animal Breeds and Species Using Bioacoustics and Artificial Neural Networks
This provides an automated tool for animal identification, biodiversity determination, and conservation efforts, but it is incremental as it applies existing methods to new data.
The researchers tackled the problem of automatically identifying animal breeds and species using bioacoustic data and artificial neural networks, achieving correct identification rates of 71.43% for birds, 94.44% for dogs, and 90.91% for frogs.
In this research endeavor, it was hypothesized that the sound produced by animals during their vocalizations can be used as identifiers of the animal breed or species even if they sound the same to unaided human ear. To test this hypothesis, three artificial neural networks (ANNs) were developed using bioacoustics properties as inputs for the respective automatic identification of 13 bird species, eight dog breeds, and 11 frog species. Recorded vocalizations of these animals were collected and processed using several known signal processing techniques to convert the respective sounds into computable bioacoustics values. The converted values of the vocalizations, together with the breed or species identifications, were used to train the ANNs following a ten-fold cross validation technique. Tests show that the respective ANNs can correctly identify 71.43\% of the birds, 94.44\% of the dogs, and 90.91\% of the frogs. This result show that bioacoustics and ANN can be used to automatically determine animal breeds and species, which together could be a promising automated tool for animal identification, biodiversity determination, animal conservation, and other animal welfare efforts.