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.
On the Importance of Passive Acoustic Monitoring Filters