Optimal feature selection and model explanation for reef fish sound classification

Fish produce a wide variety of sounds that contribute to the soundscapes of aquatic environments. In reef systems, these sounds are important acoustic cues for various ecological processes. Artificial intelligence methods to detect, classify and identify fish sounds have become increasingly common. This study proposes the classification of unknown fish sounds recorded in a subtropical rocky reef using different feature sets, data augmentation and explainable artificial intelligence tools. We used different supervised algorithms (naive Bayes, random forest, decision trees and multilayer perceptron) to perform a multiclass classification of four classes of fish pulsed sounds. The proposed models showed excellent performances, achieving 98.1% of correct classification with multilayer perceptron using data augmentation. Explainable artificial intelligence allowed us to identify which features contributed to predict each sound class. Recognizing and characterizing these sounds is key to better understanding diel behaviours and functional roles associated with critical reef ecological processes.This article is part of the theme issue 'Acoustic monitoring for tropical ecology and conservation'.