Insect Sound Recognition Based on Convolutional Neural Network

Abstract

A novel insect sound recognition system using enhanced spectrogram and convolutional neural network is proposed. Contrast-limit adaptive histogram equalization (CLAHE) is adopted to enhance R-space spectrogram. Traditionally, artificial feature extraction is an essential step of classification, introducing extra noise caused by subjectivity of individual researchers. In this paper, we construct a convolutional neural network (CNN) as classifier, extracting deep feature by machine learning. Mel-Frequency Cepstral Coefficient (MFCC) and chromatic spectrogram have been compared with enhanced R-space spectrogram as feature image. Eventually, 97.8723 % accuracy rate is achieved among 47 types of insect sound from USDA library.