Open AccessTurning wingbeat sounds into spectrum images for acoustic insect classification
Author(s) -
Zhang Chongsheng,
Wang Pengyou,
Guo Hui,
Fan Gaojuan,
Chen Ke,
Kämäräinen JoniKristian
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.3334
Subject(s) - computer science , artificial intelligence , convolutional neural network , feature extraction , speech recognition , pattern recognition (psychology) , classifier (uml) , feature (linguistics) , frequency spectrum , computer vision , telecommunications , spectral density , philosophy , linguistics
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.