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access icon free Turning wingbeat sounds into spectrum images for acoustic insect classification

© The Institution of Engineering and Technology
Received 31/08/2017, Published 14/11/2017

A novel acoustic insect classifier on deep convolutional feature of frequency spectrum images generated by their wingbeat sounds is introduced. By visualising insect wingbeat sound, the proposed method is the first attempt to convert time-series acoustic signal processing to image recognition, which has recently gained significant improvement with convolutional neural networks. Experiments show the better accuracy of the proposed method on the public UCR flying insect datasets compared with the state-of-the-art methods.

Inspec keywords: neural nets; time series; image classification; acoustic imaging; acoustic signal processing

Other keywords: image recognition; wingbeat sound tuning; convolutional neural network; insect wingbeat sound visualization; deep convolutional feature; time-series acoustic signal processing; acoustic insect classification; frequency spectrum image classification; public UCR flying insect

Subjects: Other topics in statistics; Computer vision and image processing techniques; Neural computing techniques; Image recognition; Other topics in statistics; Sonic and ultrasonic applications

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