1. Rapid expansion in the collection of large acoustic datasets to answer ecological questions has generated a parallel requirement for techniques that streamline analysis of these datasets. In many cases, automated signal recognition algorithms, often termed ‘call recognizers’, are the only feasible option for doing this. To date, most research has focused on what types of recognizers perform best, and how to train these recognizers to optimize performance.
2. We demonstrate that once recognizer construction is complete and the data processed, further improvements are possible using intrinsic and contextual information associated with each detection. We initially construct a call recognizer for the Night Parrot Pezoporus occidentalis using the r package monitoR, and scan a test dataset. We then examine a number of intrinsic variables associated with each detection generated by the recognizer, and several contextual variables associated with the species' environment and ecology, to determine if they might help predict whether a given detection is a true positive (target signal) or false positive (non‐target signal). We test several logistic regression models incorporating different combinations of intrinsic and contextual variables, selecting the best‐performing model for application. We train the model, using it to calculate the probability each detection is a true or false positive.
3. Substituting this model‐derived probability for raw recognizer score improved the recognizer's performance, reducing the number of detections requiring proofing by 60% to achieve a recall of 90%, and by 76% to achieve a recall of 75%.
4. This technique is applicable to any recognizer output, regardless of the underlying algorithm. Application requires an understanding of how the recognizer algorithm determines matches, and knowledge of a species' ecology and environment. Because advanced programming skills and expertise are not required to apply this technique, it will be particularly relevant to field ecologists for whom building and operating call recognizers is an element of their research toolbox, but not necessarily a focus.

中文翻译：

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使用与自动信号检测相关的内在和上下文信息来改善呼叫识别器的性能：使用隐秘且极度濒危的夜鹦鹉Occidentalis的案例研究

1. 大型声学数据集的收集迅速扩展以回答生态问题，对简化这些数据集的分析技术提出了并行要求。在许多情况下，通常被称为“呼叫识别器”的自动信号识别算法是唯一可行的选择。迄今为止，大多数研究都集中在哪种类型的识别器性能最佳以及如何训练这些识别器以优化性能上。
2. 我们证明，一旦识别器构造完成并处理了数据，便可以使用与每个检测相关的内在信息和上下文信息进行进一步的改进。我们最初使用r包monitoR为夜鹦鹉Pezoporus occidentalis构造了一个呼叫识别器，然后扫描测试数据集。然后，我们检查与识别器生成的每个检测相关的许多内在变量，以及与物种的环境和生态相关的几个上下文变量，以确定它们是否有助于预测给定的检测是否为真实阳性（目标信号）或假阳性（非目标信号）。我们测试了几种逻辑回归模型，这些模型结合了内在变量和上下文变量的不同组合，选择了性能最佳的应用模型。我们训练模型，使用它来计算每次检测为真假阳性的概率。
3. 用此模型得出的概率代替原始识别器分数，可以提高识别器的性能，将需要打样的检测次数减少60％，以实现90％的召回率，减少76％的概率，以实现75％的召回率。
4. 该技术适用于任何识别器输出，而与基础算法无关。应用程序需要了解识别器算法如何确定匹配项，并了解物种的生态和环境。由于不需要使用高级编程技能和专业知识即可应用此技术，因此对于构建和操作呼叫识别器是其研究工具箱中的元素但不一定要关注的领域生态学家而言，这尤其重要。