Abstract:

This study aims to provide general technicians who manage pests in production with a convenient way to recognize insects. A simple and viable scheme to identify insect voiceprints automatically is introduced using a sound parameterization technique that dominates speaker recognition technology. The acoustic signal was preprocessed and segmented into a series of sound samples.  Mel-frequency cepstrum coefficient (MFCC) was extracted from the sound sample, and a feature model was trained using Linde-Buzo-Gray algorithm to generate vector quantization (VQ) codebook from above MFCC. The matching for a test sample was completed by finding the nearest neighbour in all the VQ codebooks. The method was tested in a database with acoustic samples of 70 different insect sounds. The recognition rate above 96% was obtained, and an ideal time performance was also achieved. The test results proved the efficiency of the proposed method.

Key words: Insects, sound recognition, Mel-frequency cepstrum coefficient (MFCC), Linde-Buzo-Gray (LBG) algorithm, vector quantization (VQ)