Ecology & computer audition: applications of audio technology to monitor organisms and environment

Among the 17 Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change. Moreover, SDGs 14 and 15 claim the protection and conservation of life below water and life on land, respectively. In this work, we provide a literature-founded overview of application areas, in which computer audition – a powerful but in this context so far hardly considered technology, combining audio signal processing and machine intelligence – is employed to monitor our ecosystem with the potential to identify ecologically critical processes or states. We distinguish between applications related to organisms, such as species richness analysis and plant health monitoring, and applications related to the environment, such as melting ice monitoring or wildfire detection. This work positions computer audition in relation to alternative approaches by discussing methodological strengths and limitations, as well as ethical aspects. We conclude with an urgent call to action to the research community for a greater involvement of audio intelligence methodology in future ecosystem monitoring approaches

Soundscape in Urban Forests

This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests

Towards a multisensor station for automated biodiversity monitoring

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. (C) 2022 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie

Regulating services

No abstract available

Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

The Tallgrass Prairie Soundscape; Employing an Ecoacoustic Approach to Understand Grassland Response to Prescribed Burns and the Spatial and Temporal Patterns of Nechrophilous Invertebrate Communities

Tallgrass prairies are rapidly vanishing biodiversity hotspots for native and endemic species, yet little is known regarding how spatial and temporal variation of prairie soundscapes relates to seasonal changes, disturbance patterns and biological communities. Ecoacoustics, the study of environmental sounds using passive acoustics as a non-invasive tool for investigating ecological complexity, allows for long-term data to be captured without disrupting biological communities. Two studies were carried out by employing ecoacoustic methodology to study grassland carrion food webs and to capture the phenology of a grassland soundscape following a prescribed burn. Both studies were conducted at the Nature Conservancy’s Tallgrass Prairie Preserve (3650’N, 9625’W) and used six acoustic indices to quantify the ratio of technophony to biophony, acoustic complexity, diversity, evenness, entropy, and biological acoustic diversity from over 70,000 sound recordings. Acoustic index values were used to determine the relationship between Nicrophorus burying beetle species composition and the prairie soundscape (Chapter 1) and to determine if prescribed burning changes the composition of the soundscape over time (Chapter 2). In Chapter 1, I found that associations between Nicrophorus burying beetles and the soundscape were unique to particular species, acoustic indices and times of day. For example, N. americanus trap rates showed a positive correlation to areas of increased acoustic complexity specifically at dawn. In addition to positive associations with the soundscape, we found that N. marginatus was consistently negatively correlated to higher levels of biophony, while N. tomentosus was consistently positively correlated to places with higher levels of biophony. Although reproduction of all species examined is dependent upon securing small carrion for reproduction, I found that known habitat and activity segregation of five Nicrophorus beetle species may be reflective of the soundscape. Finally, I show that favorable habitat for a critically endangered necrophilous insect, the American burying beetle (Nicrophorus americanus) can be identified by the acoustic signature extracted from a short temporal window of its grassland ecosystem soundscape. Using the same suite of acoustic indices from Chapter 1, in Chapter 2 I examined acoustic recordings at a much larger time scale to determine distinctive acoustic events driven by biophony and geophony across a 23-week period. In addition to examining acoustic changes over time, I examined differences between 11 burned and unburned pastures. Results from this study indicate that prescribed burning does alter the soundscape, especially early in the post-burn period, but the effects are ameliorated by a significant increase in biophony as the growing and breeding season progressed into the warmer summer months. Both studies demonstrate that passive acoustic recording is a reliable method to assess relationships to acoustic communities over space and time

An investigation of mining impacts on bats in South-West England

The extraction of minerals through open-pit mining can result in sudden and extensive land use change, often posing threats to local biodiversity. Bats are particularly vulnerable to the impacts of mining, but their metapopulation structure and wide-ranging roosting habits can make it challenging to monitor local populations. Here, we investigated the impacts of habitat loss and disturbance at Drakelands open-pit mine, the first new metal mine to be established within Britain in the past 45 years. This was addressed in two parts, firstly by analysing data collected by contracted ecologists at the site, in order to identify potential short-term shifts in bat activity and to evaluate the efficacy of mitigation measures. Secondly, by monitoring bat activity in the wider landscape to identify potential further-ranging impacts of the mine on local bat populations. In conjunction with this work we incorporated a field trial of a novel bat detector designed for long-term monitoring of bat activity. The results highlighted the multitude of factors which influence bat activity at a local level, and may provide a platform for continued research into the impacts of habitat fragmentation and anthropogenic noise at a species/ genus level. The information presented here will help to inform management decision making in regards to bat conservation, both at the Drakelands site and potentially at mining operations elsewhere.Wolf Mineral

Automatic acoustic detection of birds through deep learning : the first bird audio detection challenge

Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects. Multiple methods were able to attain performance of around 88% AUC (area under the ROC curve), much higher performance than previous general‐purpose methods. With modern machine learning including deep learning, general‐purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data ̶ with no manual recalibration, and no pre‐training of the detector for the target species or the acoustic conditions in the target environment.</ol