Acoustic recordings provide detailed information regarding the behavior of cryptic wildlife to support conservation translocations

Yan, Xiao Qing; Zhang, Hemin; Li, Desheng; Wu, Daifu; Zhou, Shuiqin; Sun, Min; Hu, Haiping; Liu, Xiaoqiang; Mou, Shijie; He, Shengshan; Owen, Megan A.; Huang, Yan

doi:10.1038/s41598-019-41455-z

Cited by 13 publications

(9 citation statements)

References 64 publications

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“…Drones (Hodgson et al, 2018), GPS transmitters (Fischer et al, 2018), acoustic-monitoring (Yan et al, 2019), and environmental DNA (eDNA) sequencing (Hunter, Hoban, Bruford, Segelbacher, & Bernatchez, 2018), are among the many new technologies augmenting remote, non-invasive monitoring. Genetic insights from eDNA, paleogenomics (ancient DNA), population genomics, and functional genomics, offer robust data to enhance translocation planning and implementation ranging from identifying suitable source populations to opportunities to facilitate adaptation to threats such as introduced disease (Box 3).…”

Section: Enhancing Translocation Practice With Innovative Tools Andmentioning

confidence: 99%

U.S. conservation translocations: Over a century of intended consequences

Novak

Phelan²,

Weber³

2021

Conservat Sci and Prac

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Conservation translocations (reintroductions, reinforcements, ecological replacements, and assisted colonization) have played a vital and necessary role in conserving endangered species and ecosystems. Yet concerns over potential unintended ecological consequences frequently hinder the progress of translocation activities. We reviewed the history of U.S. translocations to ask: how often were intended benefits the result versus harmful unintended consequences? We found that translocations played a key role in recovery for 30% (14 of 47) of U.S. delisted taxa. Translocations have been performed, are planned, or are part of continuing recovery actions for 70% (1,112 of 1,580) of listed threatened and endangered taxa. Of the 1,014 total taxa we found with recorded conservation translocations spanning 125 years, we found only one restricted instance that caused a loss of biodiversity. All other reports of negative consequences were caused by translocations performed for economic and cultural interests in the absence of conservation-based governance. Examples included fish stocking for sport and biological control programs for agricultural pests. We included biological control programs in this analysis because they can be and often are used as conservation tools, to directly benefit ecosystems.

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Section: Enhancing Translocation Practice With Innovative Tools Andmentioning

confidence: 99%

U.S. conservation translocations: Over a century of intended consequences

Novak

Phelan²,

Weber³

2021

Conservat Sci and Prac

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“…The first convolution layer is followed by a pooling layer. After that, the convolution layers (4)(5)(6)(7)(8)(9)(10)(11) are stacked similar to VGG-13 [48] (two convolutions followed by a pooling layer). The final convolution layer (conv-12 ) is followed by a single average pooling layer.…”

Section: Acdnet Architecturementioning

confidence: 99%

“…Intelligent sound recognition is receiving strong interest in a growing number of application areas, from technical safety [1,2,3], surveillance and urban monitoring [4] to environmental applications [5,6].…”

Section: Introductionmentioning

confidence: 99%

Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

Mohaimenuzzaman,

Bergmeir,

West

et al. 2021

Preprint

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We introduce the first ever pipeline for sound classification using Deep Learning in extremely resource-constrained (battery powered, low memory and CPU speed) microcontrollers (MCUs).• Firstly, we present a CNN architecture (ACDNet) that achieves stateof-the-art classification accuracy on ESC-10 and ESC-50 benchmark datasets.• Secondly, we compress ACDNet using our proposed hybrid structured compression technique to obtain Micro-ACDNet that is 97.22% smaller in size and requires 97.28% less FLOPs yet produces close to state-ofthe-art performance.• Finally, after 8-bit quantization, we deploy Micro-ACDNet on a standard off-the shelf MCU.

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“…A UDIO classification is a fundamental building block of many smart Internet of Things (IoT) applications such as predictive maintenance [1]- [3], surveillance [4], [5], and ecosystem monitoring [6], [7]. Smart sensors driven by microcontroller units (MCUs) are at the core of these applications.…”

Section: Introductionmentioning

confidence: 99%

Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-Devices

2022

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Deep learning has celebrated resounding successes in many application areas of relevance to the Internet of Things (IoT), such as computer vision and machine listening. These technologies must ultimately be brought directly to the edge to fully harness the power of deep leaning for the IoT. The obvious challenge is that deep learning techniques can only be implemented on strictly resource-constrained edge devices if the models are radically downsized. This task relies on different model compression techniques, such as network pruning, quantization, and the recent advancement of XNOR-Net. This study examines the suitability of these techniques for audio classification on microcontrollers. We present an application of XNOR-Net for end-to-end raw audio classification and a comprehensive empirical study comparing this approach with pruning-and-quantization methods. We show that raw audio classification with XNOR yields comparable performance to regular full precision networks for small numbers of classes while reducing memory requirements 32-fold and computation requirements 58-fold. However, as the number of classes increases significantly, performance degrades, and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring approximately 8x more computation. We show that these insights are consistent between raw audio classification and image classification using standard benchmark sets. To the best of our knowledge, this is the first study to apply XNOR to end-to-end audio classification and evaluate it in the context of alternative techniques. All codes are publicly available on GitHub.

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Acoustic recordings provide detailed information regarding the behavior of cryptic wildlife to support conservation translocations

Cited by 13 publications

References 64 publications

U.S. conservation translocations: Over a century of intended consequences

U.S. conservation translocations: Over a century of intended consequences

Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-Devices

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