An automated multispecies bioacoustics sound classification method based on a nonlinear pattern: Twine-pat

Abstract

Categorizing the bioacoustic and ecoacoustic properties of animals is great interest to biologists and ecologists. Also, multidisciplinary studies in engineering have significantly contributed to the development of acoustic analysis. Observing the animals living in the ecological environment provides information in many areas such as global warming, climate changes, monitoring of endangered animals, agricultural activities. However, the classification of bioacoustics sounds by manually is very hard. Therefore, automated bioacoustics sound classi-fication is crucial for ecological science. This work presents a new multispecies bioacoustics sound dataset and novel machine learning model to classify bird and anuran species with sounds automatically. In this model, a new nonlinear textural feature generation function is presented by using twine cipher substitution box(S-box), and this feature generation function is named twine-pat. By using twine-pat and tunable Q-factor wavelet transform, a multilevel feature generation network is presented. Iterative ReliefF(IRF) is employed to select the most effective/valuable features. Two shallow classifiers are used to calculate results. Our presented model reached 98.75% accuracy by using k-nearest neighbor(kNN) classifier. The results obviously demonstrated the success of the presented model.