Workflow and convolutional neural network for automated identification of animal sounds
Zachary J. Ruff,Zachary J. Ruff,Damon B. Lesmeister,Damon B. Lesmeister,Cara L. Appel,Cara L. Appel,Christopher M. Sullivan +6 more
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TLDR
A graphical interface for the neural network is developed that can be run through RStudio using the Shiny package, creating a portable and user-friendly way for field biologists and managers to efficiently process audio data and detect these target species close to the point of collection and with minimal delays using consumer-grade computers.About:
This article is published in Ecological Indicators.The article was published on 2021-05-01 and is currently open access. It has received 39 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Computational bioacoustics with deep learning: a review and roadmap
TL;DR: A review of the state-of-the-art in deep learning for computational bioacoustics can be found in this article , where the authors propose a subjective but principled roadmap for computational biology with deep learning, in order to make the most of future developments in AI and informatics.
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WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection
TL;DR: In this article , a deep learning-based automated high-performance detection model for real-time endangered wildlife detection is presented, where a residual block in the CSPDarknet53 backbone is introduced for strong and discriminating deep spatial features extraction and integrate DenseNet blocks to improve in preserving critical feature information.
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Environmental Modeling: Microcomputers in environmental simulation
Journal ArticleDOI
The Rapid Rise of Next-Generation Natural History
Marie I. Tosa,Emily H. Dziedzic,Cara L. Appel,Jenny Urbina,Aimee L. Massey,Joel S. Ruprecht,Charlotte E. Eriksson,Jane E. Dolliver,Damon B. Lesmeister,Damon B. Lesmeister,Matthew G. Betts,Carlos A. Peres,Taal Levi +12 more
TL;DR: The next-generation natural history as discussed by the authors is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents, which can be leveraged and applied to conservation and management.
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Using acoustics and artificial intelligence to monitor pollination by insects and tree use by woodpeckers.
TL;DR: In this article , a convolutional neural network (CNN) was trained on spectrographic images to automatically detect the sounds of flying insects' buzzing and woodpeckers' drumming as they forage and call.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Posted Content
Decoupled Weight Decay Regularization
Ilya Loshchilov,Frank Hutter +1 more
TL;DR: This work proposes a simple modification to recover the original formulation of weight decay regularization by decoupling the weight decay from the optimization steps taken w.r.t. the loss function, and provides empirical evidence that this modification substantially improves Adam's generalization performance.
Proceedings Article
Decoupled Weight Decay Regularization.
Ilya Loshchilov,Frank Hutter +1 more
TL;DR: Recently, this paper proposed a decoupled weight decay regularization that decouples the optimal weight decay factor from the setting of the learning rate for both standard SGD and Adam and substantially improves Adam's generalization performance.
Journal ArticleDOI
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Justin Salamon,Juan Pablo Bello +1 more
TL;DR: It is shown that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a “shallow” dictionary learning model with augmentation.
Journal ArticleDOI
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Justin Salamon,Juan Pablo Bello +1 more
TL;DR: In this paper, the authors proposed a deep convolutional neural network architecture for environmental sound classification and used audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture.