Insect Sound Recognition Based on MFCC and PNN

This study aims to provide general technicians who manage pests in production with a convenient way to recognize insects. A viable scheme to identify insect sounds automatically is proposed by using sound parameterization techniques that dominate speaker recognition technology. The acoustic signal is preprocessed, segmented into a series of sound samples. Mel-frequency cepstrum coefficient(MFCC) is extracted from the sound sample as sound features, and probabilistic neural network(PNN) is trained with given features. The testing samples are classified by the PNN finally. The proposed method is evaluated in a database with acoustic samples of 50 different insect sounds. The recognition rate was above 96%. The test results proved the efficiency of the proposed method.