Automatic recognition of anuran species based on syllable identification

Monitoring of biological populations is well known for being a complex task that involves high operational costs, unknown reproductive intervals of the studied species, and difficult visualization of isolated individuals (due to their mimetic and cryptic capabilities). Therefore, the development of new methodologies able to measure quantities of individuals in specific biological populations without direct contact is desired. Species and individual recognition, based on acoustic analysis of their calls (Bioacoustics), is possible for many animals and has proven to be a useful tool in the study and monitoring of animal species. In this paper, an unsupervised methodology for anuran automatic identification is proposed; it is based on the use of a fuzzy classifier and Mel Frequency Cepstral Coefficients. This methodology is able to detect species not presented in the training stage, although they belong to different populations. Additionally, correlations among species of the same genus can be determined through the similarities of their calls. For testing the proposed method, two different datasets with species from the northeastern Colombia (Chocó and Antioquia departments with 103 and 813 mating calls respectively) were used. In validation tests performed, accuracies between 99.38% and 100% were achieved in all species by applying the proposed methodology to both datasets. Thirteen different species of anurans in both datasets were correctly identified.