NDLI: Turning wingbeat sounds into spectrum images for acoustic insect classification

Content Provider	IET Digital Library
Author	Zhang, Chongsheng Wang, Pengyou Guo, Hui Fan, Gaojuan Chen, Ke Kämäräinen, Joni Kristian
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.
Starting Page	1674
Ending Page	1676
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2017.3334
Journal	Electronics Letters
Publisher Date	2017-11-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acoustic Imaging Acoustic Insect Classification Acoustic Signal Processing Computer Vision And Image Processing Technique Convolutional Neural Network Deep Convolutional Feature Frequency Spectrum Image Classification Image Classification Image Recognition Insect Wingbeat Sound Visualization Neural Computing Technique Neural Nets Public UCR Flying Insect Sonic And Ultrasonic Application Statistics Time Series Time-series Acoustic Signal Processing Wingbeat Sound Tuning
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Deep and CNN fusion method for binaural sound source localisation

Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

Domain adaptation for ear recognition using deep convolutional neural networks

Classification of Time-Series Images Using Deep Convolutional Neural Networks

Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

Palmprint gender classification by convolutional neural network

Underwater sonar image classification using generative adversarial network and convolutional neural network

Audio classification using braided convolutional neural networks

Intelligent classification on images of Dongba ancient books

Turning wingbeat sounds into spectrum images for acoustic insect classification

Similar Documents

Deep and CNN fusion method for binaural sound source localisation

Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

Domain adaptation for ear recognition using deep convolutional neural networks

Classification of Time-Series Images Using Deep Convolutional Neural Networks

Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

Palmprint gender classification by convolutional neural network

Underwater sonar image classification using generative adversarial network and convolutional neural network

Audio classification using braided convolutional neural networks

Intelligent classification on images of Dongba ancient books

Turning wingbeat sounds into spectrum images for acoustic insect classification