Journal article
Amphibian Sounds Generating Network Based on Adversarial Learning
IEEE signal processing letters, v 27, pp 640-644
2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This letter proposes a generative network based on adversarial learning for synthesizing short-time audio streams and investigates the effectiveness of data augmentation for amphibian call sounds classification. Based on Fourier analysis, the generator is designed by a multi-layer perceptron composed of frequency basis learning layers and an output layer, and a discriminator is constructed by a convolutional neural network. Additionally, regularization on weights is introduced to train the networks with practical data that includes some disturbances. Synthetic audio streams are evaluated by quantitative comparison using inception score, and classification results are compared for real versus synthetic data. In conclusion, the proposed generative network is shown to produce realistic sounds and therefore useful for data augmentation.
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Details
- Title
- Amphibian Sounds Generating Network Based on Adversarial Learning
- Creators
- Sangwook Park - Johns Hopkins UniversityMounya Elhilali - Johns Hopkins UniversityDavid K. Han - DEVCOM Army Research LaboratoryHanseok Ko - Korea University
- Publication Details
- IEEE signal processing letters, v 27, pp 640-644
- Publisher
- IEEE
- Number of pages
- 5
- Grant note
- Korea Environment Industry & Technology Institute 2017000210001 / Korea Ministry of Environment Public Technology Program based on Environmental Policy U.S. Army Research Laboratory
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000536270500004
- Scopus ID
- 2-s2.0-85087384642
- Other Identifier
- 991021930829604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic