Automatic anuran identification using noise removal and audio activity detection. (15th April 2017)

• Record Type: Journal Article
• Authors: Alonso, Jesús B.; Cabrera, Josué; Shyamnani, Rohit; Travieso, Carlos M.; Bolaños, Federico; García, Adrián; Villegas, Alexander; Wainwright, Mark
• Is Part Of: Expert systems with applications. Volume 72(2017)
• Page Start: 83
• Page End: 92
• Publication Date: 2017-04-15
• Subjects: Bioacoustical identification -- Biodiversity monitoring -- Species richness -- Ecological indices -- Environmental audio -- Frog identification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.12.019
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Automatic anuran identification based on audio. Noise removal and audio activity detection. Real database recording in natural environment. Between 96.1% and 100% of accuracy for 17 species. Abstract: The use of bioacoustics to identify animal species has huge potential for use in biology and conservation research. Fields that could be greatly enhanced by the use of bioacoustical techniques include the study of animal behavior, soundscape ecology, species diversity assessments, and long-term monitoring - for example to further our understanding of the conservation status of numerous species and their vulnerability to different threats. In this study, we focus primarily, but not exclusively, on the identification of anuran vocalizations. We chose anurans both because they tend to be quite vocal and because they are considered indicators of environmental health. We present a system for semi-automated segmentation of anuran calls, based on sound enhancement method that uses Minimum-Mean Square Error (MMSE) Short-Time Spectral Amplitude (STSA) estimator and noise suppression algorithm using Spectral Subtraction (SS), and an automated classification system for 17 anuran species based on Mel-Frequency Cepstrum Coefficients (MFCC) and the Gaussian Mixture Model (GMM). To our knowledge this is the first study that applies this combination of methods for animal identification. This technique achieves accuracies of between 96.1% and 100% per species. Experimental results show that the semi-automated segmentation technique performs better than automated segmentation systems, improving the average success rate to 98.61%. The effectiveness of the proposed anuran identification system in natural environment is thus verified. This work presents a first approach to future tools which can signify a significant advance in the procedures to analysis in a semiautomatic or even in an automatic way to analysis indicators of environmental health based on expert and intelligent systems.