Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1674-1676 |
| Number of pages | 3 |
| Journal | Electronics Letters |
| Volume | 53 |
| Issue number | 25 |
| DOIs | |
| Publication status | Published - 2017 |
| Publication type | A1 Journal article-refereed |
Keywords
- acoustic imaging
- acoustic signal processing
- image classification
- neural nets
- time series
- acoustic insect classification
- convolutional neural network
- deep convolutional feature
- frequency spectrum image classification
- image recognition
- insect wingbeat sound visualization
- public UCR flying insect
- time-series acoustic signal processing
- wingbeat sound tuning
Publication forum classification
- Publication forum level 1