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DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals.

机译：DCL系统使用深度学习方法进行陆基或船舶实时识别和海洋哺乳动物的本地化。

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The ONR DCL grant focuses on advancing the state of the art for bioacoustic signal detection and classification through researching new technologies, algorithms and systems. This work engages a unique team of experts from Cornell University (CU), New York University (NYU) and Northeast Fisheries Science Center (NEFSC). Aimed at developing new methods and practices for advancing detection classification (DC), the grant team also maintains a higher level goal: addressing general data mining strategies as applied to large, complex acoustic datasets. The underlying focus of the team s work is to integrate and develop new technologies for hardware and software tools based on high performance computing, and reduce these to practice through outreach into the broader bioacoustic community.

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作者
Dugan, P. J.; Clark, C. W.; LeCun, Y. A.; Van Parijs, S. M.;
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年度 2014
页码 1-11
总页数 11
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Acoustic detection; Aquatic animals; Bioacoustics; Learning machines; Acoustic detectors; Algorithms; High performance computing; Real time; Real-time recognition; Localization; Marine mammals; Hpc ada(high performance computing acoustic data accelerator); Delma(detection classification and machine learning algorithms);

机译：声学检测;水生动物;生物声学;学习机器;声学探测器;算法;高性能计算;实时;实时识别;本地化;海洋哺乳动物; Hpc ada（高性能计算声学数据加速器）; Delma（检测分类和机器）学习算法）;

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机译：基于深度学习的本地化和感知系统：大规模的自动货物运输车辆的方法
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5. Source localization of narrow band signals in multipath environments, with application to marine mammals. [D] . Valtierra, Robert Daniel. 2013

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8. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. [R] . Dugan, P. J., Clark, C. W., LeCun, Y. A., 2012

机译：DCL系统使用深度学习方法进行陆基或船舶实时识别和海洋哺乳动物的本地化。

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals.

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