Title: Classification of insects' acoustic signals using a hybrid approach: Mel-frequency Hilbert-Huang transformation

Authors: Rekha Kaushik; Jyoti Singhai

Addresses: Department of Electronics and Communication Engineering, MANIT, Bhopal, 462023, India ' Department of Electronics and Communication Engineering, MANIT, Bhopal, 462023, India

Abstract: Insects present in stored grain, wood, soil, plants, and environments have distinctive sets of acoustic features. This paper developed an insect detection and classification system using their sound dataset. A novel approach has been proposed based on the combination of features: Mel-frequency cepstral coefficient and Hilbert Huang transform, named Mel-frequency Hilbert Huang Transform (MFHTT) for acoustic feature extraction. The proposed method integrates the ability of principal component analysis (PCA) to reduce the dimensions and de-correlate the coefficients for insect sound classification. Support vector machine (SVM), K-nearest neighbour (KNN), Random Forest, Naïve Bayes and neural network have been analysed for achieving the highest detection accuracy. Experimental results show that the proposed feature extraction method performed better than baseline features and achieved an improved accuracy of 23.19% with the classifier KNN. Also, KNN outperforms as compared to other classifiers with a detection accuracy of 98.8%.

Keywords: acoustic sensing; classification algorithms; feature extraction; Hilbert-Huang transformation; insect; Mel-frequency cepstral coefficient.

DOI: 10.1504/IJAACS.2024.142521

International Journal of Autonomous and Adaptive Communications Systems, 2024 Vol.17 No.6, pp.489 - 501

Received: 19 Mar 2022
Accepted: 09 Nov 2022
Published online: 06 Nov 2024 *

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