We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based detection.

We used passive acoustic monitoring in a pre-montane forest in Peru to investigate how soundscape composition and richness of acoustic frequencies varied with distance from a natural gas exploratory well. Results demonstrate that acoustic monitoring and soundscape analyses are useful tools for evaluating the impact of development activity on the vocalizing community, and should be implemented as a best practice in monitoring biodiversity and for guiding specific mitigation strategies.

In this paper, we describe a new spectrogram-based approach for extracting individual audio events. Our goal is to develop an algorithm that is not sensitive to noise, does not need any prior training data and works with any type of audio event. To do this, we propose: (1) a spectrogram filtering method, the Flattened Local Trimmed Range (FLTR) method, which models the spectrogram as a mixture of stationary and non-stationary energy processes and mitigates the effect of the stationary processes, and (2) an unsupervised algorithm that uses the filter to detect audio events.

Artisanal and small-scale gold mining (ASM) is becoming a significant cause of environmental degradation in tropical ecosystems. In this study, we conducted a rapid assessment on the impact of an ASM gold mine on the vocalizing avian and anuran communities in the buffer zone of the Tambopata National Reserve in Peru.

The combination of acoustic monitoring to improve species detectability and statistical methods to account for false-negative detections can improve species distribution estimates. Here, we combine a novel automated species-specific identification approach with occupancy models that account for imperfect detectability to provide a more accurate species distribution map of the Elfin Woods Warbler Setophaga angelae, a rare, elusive and threatened bird species.

Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON), a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings.