关键词: 昆虫, 种类鉴定, 声音处理, 自动识别, 梅尔倒谱系数, 混合高斯模型

Abstract: Insects produce various sounds when they are moving, feeding or calling. These sounds exhibit intraspecies similarity and interspecies differences, thus they can be used to discriminate species identities of insects. Automatic detection of insect species through sounds produced by the insects would be very meaningful in giving farm workers or forestry workers a convenient way to recognize insects. In this study we employed the sound parameterization techniques that are frequently used in the field of human speech recognition. Melfrequency cepstrum coefficients (MFCCs) were extracted from the sound samples after preprocessing, and Gaussian mixture model (GMM) was trained with these MFCC features. Finally, the unknown insect sound samples were classified by the GMM. The proposed method was evaluated in a database with acoustic samples of 58 different insect sounds. The method performed well in terms of both recognition rate and time performance. The average recognition accuracy was as high as 98.95%. The test results proved that sound parameterization techniques based on MFCC and GMM could be used to recognize insect species efficiently.

Key words: Insects, species identification, sound processing, automatic recognition, Melfrequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM)