Automated recognition is increasingly used to extract species detections from audio recordings; however, the time required to manually review each detection can be prohibitive. We developed a flexible protocol called “validation prediction” that uses machine learning to predict whether recognizer detections are true or false positives and can be applied to any recognizer type, ecological application, or analytical approach. Validation prediction uses a predictable relationship between recognizer score and the energy of an acoustic signal but can also incorporate any other ecological or spectral predictors (e.g., time of day, dominant frequency) that will help separate true from false‐positive recognizer detections. First, we documented the relationship between recognizer score and the energy of an acoustic signal for two different recognizer algorithm types (hidden Markov models and convolutional neural networks). Next, we demonstrated our protocol using a case study of two species, the Common Nighthawk (Chordeiles minor) and Ovenbird (Seiurus aurocapilla). We reduced the number of detections that required validation by 75.7% and 42.9%, respectively, while retaining at least 98% of the true‐positive detections. Validation prediction substantially improves the efficiency of using automated recognition on acoustic data sets. Our method can be of use to wildlife monitoring and research programs and will facilitate using automated recognition to mine bioacoustic data sets.

中文翻译：

---

验证预测：一种灵活的协议，可提高野生动植物研究的自动声学处理效率。

自动识别越来越多地用于从录音中提取物种检测。但是，手动检查每个检测结果所需的时间可能会令人望而却步。我们开发了一种灵活的协议，称为“验证预测”，该协议使用机器学习来预测识别器检测是真还是假阳性，并且可以应用于任何类型的识别器，生态应用或分析方法。验证预测使用识别器得分与声音信号能量之间的可预测关系，但也可以合并任何其他生态或频谱预测器（例如，一天中的时间，主导频率），这将有助于区分真假识别器检测。第一，我们记录了两种不同的识别器算法类型（隐马尔可夫模型和卷积神经网络）识别器得分与声音信号能量之间的关系。接下来，我们通过对两种物种的案例研究来证明我们的实验方案，即普通夜鹰（小Chordeiles）和Ovenbird（Seiurus aurocapilla）。我们将需要验证的检测数量分别减少了75.7％和42.9％，同时保留了至少98％的真实阳性检测。验证预测大大提高了在声学数据集上使用自动识别的效率。我们的方法可用于野生动植物监测和研究计划，并将有助于使用自动识别来挖掘生物声数据集。