From Soundwave to Soundscape: A Guide to Acoustic Research in Captive Animal Environments

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

Pied tamarins change their vocal behavior in response to noise levels in the largest city in the Amazon

Many animal species depend on sound to communicate with conspecifics. However, human-generated (anthropogenic) noise may mask acoustic signals and so disrupt behavior. Animals may use various strategies to circumvent this, including shifts in the timing of vocal activity and changes to the acoustic parameters of their calls. We tested whether pied tamarins (Saguinus bicolor) adjust their vocal behavior in response to city noise. We predicted that both the probability of occurrence and the number of long calls would increase in response to anthropogenic noise and that pied tamarins would temporally shift their vocal activity to avoid noisier periods. At a finer scale, we anticipated that the temporal parameters of tamarin calls (e.g., call duration and syllable repetition rate) would increase with noise amplitude. We collected information on the acoustic environment and the emission of long calls in nine wild pied tamarin groups in Manaus, Brazil. We found that the probability of long-call occurrence increased with higher levels of anthropogenic noise, though the number of long calls did not. The number of long calls was related to the time of day and the distance from home range borders-a proxy for the distance to neighboring groups. Neither long-call occurrence nor call rate was related to noise levels at different times of day. We found that pied tamarins decreased their syllable repetition rate in response to anthropogenic noise. Long calls are important for group cohesion and intergroup communication. Thus, it is possible that the tamarins emit one long call with lower syllable repetition, which might facilitate signal reception. The occurrence and quantity of pied tamarin' long calls, as well as their acoustic proprieties, seem to be governed by anthropogenic noise, time of the day, and social mechanisms such as proximity to neighboring groups.