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

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

References

    1. 1)
    2. 2)
      • 6. Sun, Y., Chen, Y., Wang, X., et al: ‘Deep learning face representation from predicting 10,000 classes’. Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, December 2014, pp. 19881996, doi: 10.1109/CVPR.2014.244.
    3. 3)
      • 1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems (NIPS), Nevada, USA, December 2012, pp. 10971105, doi: 10.1145/3065386.
    4. 4)
      • 3. Girshick, R., Donahue, J., Darrell, T.: ‘Rich feature hierarchies for accurate object detection and semantic segmentation’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Ohio, USA, June 2014, pp. 580587, doi: 10.1109/cvpr.2014.81.
    5. 5)
      • 2. Ouyang, W., Zeng, X., Wang, X.: ‘Modeling mutual visibility relationship in pedestrian detection’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Oregon, USA, June 2013, pp. 32223229, doi: 10.1109/cvpr.2013.414.
    6. 6)
      • 5. Wikipedia Inc.: ‘Hann Window’. Available at https://en.wikipedia.org/wiki/Hann_function, accessed August 2017.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.3334
Loading

Related content

content/journals/10.1049/el.2017.3334
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading