Assessing the performance of a semi‐automated acoustic monitoring system for primates

• Published in: Methods in ecology and evolution Vol. 6; no. 7; pp. 753 - 763
• Main Authors: Heinicke, Stefanie, Kalan, Ammie K., Wagner, Oliver J.J., Mundry, Roger, Lukashevich, Hanna, Kühl, Hjalmar S., Jones, Kate
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.07.2015
• Subjects: Acoustic noise; Acoustics; automated signal recognition; Automation; bioacoustics; Cercopithecus diana; chimpanzee drumming; Classification; Colobus polykomos; Data processing; Gaussian mixture model; Marine; Marine mammals; Monitoring; Monitoring systems; Monkeys & apes; National parks; Pan troglodytes; Performance evaluation; primate vocalization; Primates; Probabilistic models; Procolobus badius; Rainforests; Recall; Segmentation; Sound; speaker segmentation; Species; species identification algorithm; support vector machine; Support vector machines; Temporal distribution; Terrestrial environments; Cote d'Ivoire, Tai Natl. Park; ASE, Cote d'Ivoire
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.12384

Summary Passive acoustic monitoring is frequently used for marine mammals, and more recently it has also become popular for terrestrial species. Key advantages are the monitoring of (1) elusive species, (2) different taxa simultaneously, (3) large temporal and spatial scales, (4) with reduced human presence and (5) with considerable time saving for data processing. However, terrestrial sound environments can be highly complex; they are very challenging when trying to automatically detect and classify vocalizations because of low signal‐to‐noise ratios. Therefore, most studies have used manual preselection of high‐quality sounds to achieve better classification rates. Consequently, most systems have never been validated under realistic field conditions. In this study, we evaluated the performance of a passive acoustic monitoring system for four primate species in the highly noisy rain forest environment of the Taï National Park, Côte d'Ivoire. We collected 12 851 h of recordings with 20 autonomous recording units and did not preselect high‐quality sounds manually. To automatically detect and classify the sounds of interest, we used an automated system built on speaker segmentation, support vector machines and Gaussian mixture models. One hundred and seventy‐nine hours of recordings were used for validating the system. The system performed well in detecting the loud calls of Cercopithecus diana and Colobus polykomos with a recall of 50% and 42%, respectively. Recall rates were lower for Pan troglodytes and Procolobus badius. To determine the presence of Cercopithecus diana and Colobus polykomos with a certainty of P > 0·999, 2 and 7 h of recordings were needed, respectively. For these two species, our automated approach reflected the spatio‐temporal distribution of vocalization events well. Despite the seemingly low precision, time investment for the manual removal of false positives in the system's output was only 3·5% compared to a human collecting and processing the primate vocalization data. The proposed monitoring system is already fully applicable for Cercopithecus diana and Colobus polykomos, whereas it needs further improvement for the other species tested. In principle, it can be applied to any distinctive animal sound and can be implemented for the collection of acoustic data for behavioural, ecological and conservation studies.