Vocalization data and scripts to model reindeer rut activity using on-animal acoustic recorders and machine learning

For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck. To expedite this process, researchers have turned to machine learning techniques, specifically neural networks, to streamline the analysis of data. Nevertheless, much of the existing research has focused predominantly on stationary recording devices, overlooking the potential benefits of employing on-animal recorders in conjunction with machine learning. To showcase the synergy of on-animal recorders and machine learning, we conducted a study at the Kutuharju research station in Kaamanen, Finland, where the vocalizations of rutting..., Bioacoustics data were collected during the rutting seasons of 2019 and 2020. In 2019, vocalizations were captured from two males, while in 2020, vocalizations were obtained from six males. However, the sampling time varied from three days to two months due to equipment issues. Moreover, challenges with the recorders limited our data collection to only seven male reindeer (two from 2019 and five from 2020).
During the translocation of the males, they were outfitted with on-animal acoustic recorders. These devices housed SOROKA-15E recording units from TS-Market Ltd., Zelenograd, Russia, capable of capturing the animals’ vocalizations with an amplitude resolution of 16 bits and a sampling rate of 16 kHz (Figure 2). The recorders enabled us to obtain continuous audio data throughout the breeding period. For storage purposes, each recorder was equipped with a 256-gigabyte microSD card, providing the capacity to record over 92 days of audio. Furthermore, a 9,000-milliamp-hour, 3.6-volt lith..., , # Vocalization data and scripts to model reindeer rut activity using on-animal acoustic recorders and machine learning

\***If the user wishes to just analyze the data, simply download the compressed in the root file***

\****During the Data upload process, the data and code had to be uploaded separately. Consequently, to place the back into software folders, simply download the corresponding compressed data folder and place the uncompressed data folder within the root folder of the specific folder it corresponds to (e.g., the  2-Rut Analysis (Data) folder into the 2-Rut Analysis and rename the  2-Rut Analysis (Data) folder to simply ‘data’ so that the software can find and pull data from said folder)

**0 - File_Annotation**

This file is separated into 4 subfolders, each similar to the other. The two SVL folders contain files that are easily imported and read by Sonic Visualizer. The other two are the same, but they are in CSV format in case a user wishes to train another machine-lear...