Ocean Acoustical Services and Instrumentation Systems, Inc., 5 Militia Drive, Lexington, Massachusetts 02421, USA.
J Acoust Soc Am. 2012 Sep;132(3):1502-10. doi: 10.1121/1.4742715.
This paper presents recent experimental results and a discussion of system enhancements made to the real-time autonomous humpback whale detector-classifier algorithm first presented by Abbot et al. [J. Acoust. Soc. Am. 127, 2894-2903 (2010)]. In February 2010, a second-generation system was deployed in an experiment conducted off of leeward Kauai during which 26 h of humpback vocalizations were recorded via sonobuoy and processed in real time. These data have been analyzed along with 40 h of humpbacks-absent data collected from the same location during July-August 2009. The extensive whales-absent data set in particular has enabled the quantification of system false alarm rates and the measurement of receiver operating characteristic curves. The performance impact of three enhancements incorporated into the second-generation system are discussed, including (1) a method to eliminate redundancy in the kernel library, (2) increased use of contextual analysis, and (3) the augmentation of the training data with more recent humpback vocalizations. It will be shown that the performance of the real-time system was improved to yield a probability of correct classification of 0.93 and a probability of false alarm of 0.004 over the 66 h of independent test data.
本文介绍了对 Abbott 等人首次提出的实时自主座头鲸检测分类算法的最新实验结果和系统增强的讨论[J. Acoust. Soc. Am. 127, 2894-2903 (2010)]。2010 年 2 月，第二代系统在考艾岛背风侧的一次实验中部署，在该实验中，通过声纳浮标记录了 26 小时的座头鲸发声，并实时进行了处理。这些数据与 2009 年 7 月至 8 月在同一地点收集的 40 小时座头鲸缺失数据一起进行了分析。特别是大量的座头鲸缺失数据集使系统误报率的量化和接收机工作特性曲线的测量成为可能。讨论了第二代系统中包含的三种增强功能的性能影响，包括 (1) 消除核库冗余的方法，(2) 增加上下文分析的使用，以及 (3) 用最近的座头鲸发声来扩充训练数据。结果表明，实时系统的性能得到了提高，在 66 小时的独立测试数据中，正确分类的概率为 0.93，误报的概率为 0.004。
