1. Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded – recognizing calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone – are not well‐developed yet.
2. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimize false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again.
3. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel‐Frequency Cepstral Coefficients) on real‐world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hr of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise.
4. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large‐scale monitoring of animal species. It can be trained to recognize new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.

中文翻译：

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小波滤波器可自动识别长时间野外记录中的鸟鸣

1. 生态声学有潜力以相对较低的成本提供有关许多动物种类丰富的大量信息。声记录单元广泛用于现场数据收集中，但是可靠处理记录数据的功能（识别相对少见的呼叫，并且通常由于噪声和与麦克风的距离而明显恶化）尚不完善。
2. 我们提出了一种用于连续现场录音的呼叫检测方法，该方法可以快速轻松地在新物种上进行训练，并随着噪声或与麦克风距离的增加而优雅地降低。该方法基于从小波节点的子集（小波包分解树中的元素）重构声音。它旨在用作预处理过滤器，因此我们旨在最大程度地减少误报：可以在后续处理中消除误报，但不会再次查看未接来电。
3. 我们将我们的方法与标准呼叫检测方法以及机器学习方法（使用小波能量或梅尔频率倒谱系数作为输入特征）进行比较，比较了六种鸟类在真实世界中的嘈杂场记录。结果表明，与其他方法相比，我们的方法具有更高的查全率（检测到的比例）：87％的样本对> 53小时的测试数据具有85％的特异性，从而使需要进一步验证的数据量减少了80％。即使背景噪音很高，它也能检测到> 60％的极其微弱（远处）的呼叫。
4. 我们的AviaNZ生物声学分析程序中提供了这种预处理方法，使用户能够大大减少后续分析所需的处理量（无论是手动还是自动），以分析通过时空大规模监测动物物种收集的连续现场记录。可以训练它轻松识别新物种，如果同时寻找多个物种，则可以并行运行过滤器。