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Turning wingbeat sounds into spectrum images for acoustic insect classification

Research output: Contribution to journalArticleScientificpeer-review

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Turning wingbeat sounds into spectrum images for acoustic insect classification. / Zhang, C.; Wang, P.; Guo, H.; Fan, G.; Chen, K.; Kämäräinen, J.-K.

In: Electronics Letters, Vol. 53, No. 25, 2017, p. 1674-1676.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Zhang, C, Wang, P, Guo, H, Fan, G, Chen, K & Kämäräinen, J-K 2017, 'Turning wingbeat sounds into spectrum images for acoustic insect classification', Electronics Letters, vol. 53, no. 25, pp. 1674-1676. https://doi.org/10.1049/el.2017.3334

APA

Zhang, C., Wang, P., Guo, H., Fan, G., Chen, K., & Kämäräinen, J-K. (2017). Turning wingbeat sounds into spectrum images for acoustic insect classification. Electronics Letters, 53(25), 1674-1676. https://doi.org/10.1049/el.2017.3334

Vancouver

Author

Zhang, C. ; Wang, P. ; Guo, H. ; Fan, G. ; Chen, K. ; Kämäräinen, J.-K. / Turning wingbeat sounds into spectrum images for acoustic insect classification. In: Electronics Letters. 2017 ; Vol. 53, No. 25. pp. 1674-1676.

Bibtex - Download

@article{23b9ea15541140f4b611d6f95374996b,
title = "Turning wingbeat sounds into spectrum images for acoustic insect classification",
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.",
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",
author = "C. Zhang and P. Wang and H. Guo and G. Fan and K. Chen and J.-K. K{\"a}m{\"a}r{\"a}inen",
year = "2017",
doi = "10.1049/el.2017.3334",
language = "English",
volume = "53",
pages = "1674--1676",
journal = "Electronics Letters",
issn = "0013-5194",
publisher = "Institution of Engineering and Technology",
number = "25",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Turning wingbeat sounds into spectrum images for acoustic insect classification

AU - Zhang, C.

AU - Wang, P.

AU - Guo, H.

AU - Fan, G.

AU - Chen, K.

AU - Kämäräinen, J.-K.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - acoustic imaging

KW - acoustic signal processing

KW - image classification

KW - neural nets

KW - time series

KW - acoustic insect classification

KW - convolutional neural network

KW - deep convolutional feature

KW - frequency spectrum image classification

KW - image recognition

KW - insect wingbeat sound visualization

KW - public UCR flying insect

KW - time-series acoustic signal processing

KW - wingbeat sound tuning

U2 - 10.1049/el.2017.3334

DO - 10.1049/el.2017.3334

M3 - Article

VL - 53

SP - 1674

EP - 1676

JO - Electronics Letters

JF - Electronics Letters

SN - 0013-5194

IS - 25

ER -