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An investigation in acoustic insect recognition

Abstract This study aims to provide general technicians who manage pests in production with a convenient way to recognize insects. Several viable schemes to identify insect sounds automatically are introduced using sound parameter standardization techniques that dominate speaker recognition technology. The acoustic signal is preprocessed, segmented into a series of sound samples. Melfrequency cepstrum coefficient (MFCC) and Sub-band based cepstral (SBC) are extracted, respectively from the sound samples, and Vector Quantization(VQ) codebook and Hidden Markov Model(HMM) are trained with given features. The matching for a test sample is completed by finding the nearest neighbour in all the VQ codebooks or the best matcher in all HMMs. These methods are evaluated and compared in a database with acoustic samples of 70 different insect sounds. The MFCC and HMM based methods demonstrated their better performance, whose recognition accuracy exceeds 98%.