Voice activity detection in eco-acoustic data enables privacy protection and is a proxy for human disturbance

1. Eco-acoustic monitoring is increasingly being used to map biodiversity across large scales, yet little thought is given to the privacy concerns and potential scientific value of inadvertently recorded human speech. Automated speech de tection is possible using voice activity detection (VAD) models, but it is not clear how well these perform in diverse natural soundscapes. In this study we pre sent the first evaluation of VAD models for anonymization of eco-acoustic data and demonstrate how speech detection frequency can be used as one potential measure of human disturbance. 2. We first generated multiple synthetic datasets using different data preprocess ing techniques to train and validate deep neural network models. We evaluated the performance of our custom models against existing state-of-the-art VAD models using playback experiments with speech samples from a man, woman and child. Finally, we collected long-term data from a Norwegian forest heavily used for hiking to evaluate the ability of the models to detect human speech and quantify a proxy for human disturbance in a real monitoring scenario. 3. In playback experiments, all models could detect human speech with high accu racy at distances where the speech was intelligible (up to 10 m). We showed that training models using location specific soundscapes in the data preprocessing step resulted in a slight improvement in model performance. Additionally, we found that the number of speech detections correlated with peak traffic hours (using bus timings) demonstrating how VAD can be used to derive a proxy for human disturbance with fine temporal resolution. 4. Anonymizing audio data effectively using VAD models will allow eco-acoustic monitoring to continue to deliver invaluable ecological insight at scale, while minimizing the risk of data misuse. Furthermore, using speech detections as a proxy for human disturbance opens new opportunities for eco-acoustic moni toring to shed light on nuanced human–wildlife interactionspublishedVersio

Snowmobile noise alters bird vocalization patterns during winter and pre-breeding season

Noise pollution poses a significant threat to ecosystems worldwide, disrupting animal communication and causing cascading effects on biodiversity. In this study, we focus on the impact of snowmobile noise on avian vocalizations during the non-breeding winter season, a less-studied area in soundscape ecology. We developed a pipeline relying on deep learning methods to detect snowmobile noise and applied it to a large acoustic monitoring dataset collected in Yellowstone National Park. Our results demonstrate the effectiveness of the snowmobile detection model in identifying snowmobile noise and reveal an association between snowmobile passage and changes in avian vocalization patterns. Snowmobile noise led to a decrease in the frequency of bird vocalizations during mornings and evenings, potentially affecting winter and pre-breeding behaviours such as foraging, predator avoidance and successfully finding a mate. However, we observed a recovery in avian vocalizations after detection of snowmobiles during mornings and afternoons, indicating some resilience to sporadic noise events. Synthesis and applications: Our findings emphasize the need to consider noise impacts in the non-breeding season and provide valuable insights for natural resource managers to minimize disturbance and protect critical avian habitats. The deep learning approach presented in this study offers an efficient and accurate means of analysing large-scale acoustic monitoring data and contributes to a comprehensive understanding of the cumulative impacts of multiple stressors on avian communities.Snowmobile noise alters bird vocalization patterns during winter and pre-breeding seasonpublishedVersio

Automated acoustic monitoring of ecosystems

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. In this thesis, we explore how recording and analysing the sounds of an environment provides a tractable solution to scalable, fully automated ecological monitoring. First, we tackle the problem of autonomous data collection, and develop a device which is able to continuously collect and remotely transmit data from field sites over long time-periods. We then move to the automated analysis of eco-acoustic data, and exploit a learned acoustic feature embedding to achieve accurate monitoring of ecosystem health across multiple spatial and temporal scales. We demonstrate that an unsupervised approach using the same acoustic feature space allows automatic identification of anomalous sounds, including hallmarks of illegal activity such as gunshots and chainsaws. Functional real-time ecological monitoring requires significant computational infrastructure, and we detail the open-source design and implementation of SAFE Acoustics, an eco-acoustic monitoring network in the tropical rainforests of Borneo. Within the ecosystems we study, species movement and behaviour are constrained by habitat structure and connectivity. However, investigating how topology influences a complex network’s behaviour is not a challenge unique to ecology. We investigate the link between structure and function in a model system, the mouse brain, and find that inter-regional axonal connectivity is closely related to the intrinsic dynamics of individual brain areas. Similar studies inspecting the dynamics of data from large-scale ecological monitoring networks may provide a fruitful avenue for further explorations.Open Acces

Soundscapes predict species occurrence in tropical forests

Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g. for lesser studied or silent species). A new approach is needed that rapidly predicts species occurrence using smaller and more coarsely labelled audio datasets. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species in 20 min recordings across a tropical forest degradation gradient in Sabah, Malaysia. Using acoustic features derived from a convolutional neural network (CNN), we characterised species indicative soundscapes by training our models on a temporally coarse labelled point-count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics from 0.53 up to 0.87. The highest accuracies were achieved for species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above-ground carbon density – a metric often used to quantify habitat quality across forest degradation gradients. Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species and provide a new direction for data driven large-scale assessments of habitat suitability. bioacoustics, machine learning, soundscape, species occurrence, tropical fores