Passive acoustic monitoring (PAM) is a noninvasive technique to supervise wildlife. Acoustic surveillance is preferable in some situations such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example to identify species based on audio recordings. However, some care should be taken to evaluate the capability of a system. We defined PAM filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise patterns, and recording devices in both the training and test sets. It is important to remark that the filters proposed here were not intended to improve the accuracy rates. Indeed, these filters tended to make it harder to obtain better rates, but at the same time, they tended to provide more reliable results. In our experiments, a random division of a database presented accuracies much higher than accuracies obtained with protocols generated with PAM filters, which indicates that the classification system learned other components presented in the audio. Although we used the animal vocalizations, in our method, we converted the audio into spectrogram images, and after that, we described the images using the texture. These are well-known techniques for audio classification, and they have already been used for species classification. Furthermore, we performed statistical tests to demonstrate the significant difference between the accuracies generated with and without PAM filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online.

中文翻译：

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无源声学监测滤波器的重要性

被动声学监测 (PAM) 是一种监测野生动物的非侵入性技术。在某些情况下，例如海洋哺乳动物，当动物大部分时间都在水下时，声学监视是更可取的，因此很难获得它们的图像。机器学习对 PAM 非常有用，例如根据录音识别物种。但是，在评估系统的能力时应该小心谨慎。我们将 PAM 过滤器定义为根据记录的日期和位置创建实验协议，旨在避免在训练和测试集中使用相同的个体、噪声模式和记录设备。重要的是要注意这里提出的过滤器并不是为了提高准确率。确实，这些过滤器往往难以获得更好的费率，但同时，它们往往能提供更可靠的结果。在我们的实验中，数据库的随机划分呈现的准确度远高于使用 PAM 过滤器生成的协议获得的准确度，这表明分类系统学习了音频中呈现的其他组件。尽管我们使用了动物的发声，但在我们的方法中，我们将音频转换为频谱图图像，然后使用纹理描述图像。这些是众所周知的音频分类技术，它们已经被用于物种分类。此外，我们进行了统计测试，以证明使用和不使用 PAM 过滤器与几个众所周知的分类器生成的准确度之间的显着差异。