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Abstract Scientists are using acoustic monitoring to assess the impact of altered soundscapes on wildlife communities and human systems. In the soundscape ecology field, monitoring and analyses approaches rely on the interdisciplinary intersection of ecology, acoustics, and computer science. Combining theory and practice of each field in the context of Knowledge Discovery in Databases (KDD), soundscape ecologists provide innovative monitoring solutions for ecologically-driven research questions. We propose a soundscape content analysis framework for improved knowledge outcome with assistance of the new multi-label (ML) concept. Here, we investigated the effectiveness of a ML k -nearest neighbor algorithm (ML- k NN) for labeling concurrent soundscape components within a single recording. We manually labeled 1200 field recordings for the presence of soundscape components and extracted ecological acoustic features, audio profile features, and Gaussian-mixture model features for each recording. Then, we tested the ML- k NN algorithm accuracy with well-established metrics adapted to ML learning. We found that seventeen unique acoustic features could predict a set of biophonic, geophonic, and anthrophonic labels for a single field recording with average precision of 0.767. However, certain labels were predicted incorrectly depending on the time of day and co-occurrence of that label with another label, suggesting further refinement is needed to improve the accuracy of predicted labels. Overall, this ML classification approach could enable researchers to label field recordings more quickly and generate an "alert" system for monitoring changes in a specific sound class. Ultimately, the adaptation of the ML algorithm may provide soundscape ecologists with new metadata labels that are searchable in large databases of soundscape field recordings. Highlights • Acoustic features with multi-label (ML) k- nearest neighbor algorithm could predict a set of labels for a single field recording. • ML algorithm could provide output of metadata labels that are searchable in large databases of soundscape field recordings. • ML algorithm uses training data to assist in faster multi-label classification of field recordings. [ABSTRACT FROM AUTHOR]

Abstract We present here the potential synergies between a sub-discipline of music called computational musicology and its associated toolkit Music Information Retrieval (MIR) systems and the current needs of soundscape ecologists who use passive acoustic recorders to collect massive amounts of audio data. These cross-sub-disciplinary synergies fall into three broad methodological categories: (1) automated feature extraction, (2) sound classification and labeling using machine learning, and (3) data visualization. We argue that the use of the MIR toolkit by soundscape ecologists could represent the development of a second generation of soundscape metrics, which are more translatable to policy and natural resource management problems. Two companion articles in this special issue by the authors provide a more detailed examination of the potential for computational musicology's MIR toolkit as applied to questions common to the emerging field of soundscape ecology. Highlights • Computational musicologists use Music Information Retrieval (MIR) systems for sound analysis that could benefit soundscape ecology. • synergies include feature extraction, sound classification and labeling using machine learning, and data visualization. • We suggest a way forward where soundscape ecologists leverage MIR tools for research and data sharing. [ABSTRACT FROM AUTHOR]

Abstract Soundscape ecology evaluates biodiversity and environmental disturbances by investigating the interaction among soundscape components (biological, geophysical, and human-produced sounds) using data collected with autonomous recording units. Current analyses consider the acoustic properties of frequency and amplitude resulting in varied metrics, but rarely focus on the discrimination of soundscape components. Computational musicologists analyze similar data but consider a third acoustic property, timbre. Here, we investigated the effectiveness of spectral timbral analysis to distinguish among dominant soundscape components. This process included manually labeling and extracting spectral timbral features for each recording. Then, we tested classification accuracy with linear and quadratic discriminant analyses on combinations of spectral timbral features. Different spectral timbral feature groups distinguished between biological, geophysical, and manmade sounds in a single field recording. Furthermore, as we tested different combinations of spectral timbral features that resulted in both high and very low accuracy results, we found that they could be ordered to "sift" out field recordings by individual dominant soundscape component. By using timbre as a new acoustic property in soundscape analyses, we could classify dominant soundscape components effectively. We propose further investigation into a sifting scheme that may allow researchers to focus on more specific research questions such as understanding changes in biodiversity, discriminating by taxonomic class, or to inspect weather-related events. Highlights • Spectral timbral features used in combination together can differentiate between major soundscape classes. • Classification method distinguishes between classes without template matching or species identification. • Sifting method from resulting discriminant analysis feature groups provides new direction for big data sorting. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge that can be gained from acoustic data collection in tropical ecosystems is low‐hanging fruit. There is every reason to record and with every day, there are fewer excuses not to do it. In recent years, the cost of acoustic recorders has decreased substantially (some can be purchased for under US$50, e.g., Hill et al. 2018) and the technology needed to store and analyze acoustic data is continuously improving (e.g., Corrada Bravo et al. 2017, Xie et al. 2017). Soundscape recordings provide a permanent record of a site at a given time and contain a wealth of invaluable and irreplaceable information. Although challenges remain, failure to collect acoustic data now in tropical ecosystems would represent a failure to future generations of tropical researchers and the citizens that benefit from ecological research. In this commentary, we (1) argue for the need to increase acoustic monitoring in tropical systems; (2) describe the types of research questions and conservation issues that can be addressed with passive acoustic monitoring (PAM) using both short‐ and long‐term data in terrestrial and freshwater habitats; and (3) present an initial plan for establishing a global repository of tropical recordings. [ABSTRACT FROM AUTHOR]

Context: While remote sensing imagery is effective for quantifying land cover changes across large areas, its utility for directly assessing the response of animals to disturbance is limited. Soundscapes approaches—the recording and analysis of sounds in a landscape—could address this shortcoming.Objectives: In 2011, a massive wildfire named “the Horseshoe 2 Burn” occurred in the Chiricahua National Monument, Arizona, USA. We evaluated the impact of this wildfire on acoustic activity of animal communities.Methods: In 2013, soundscape recordings were collected over 9 months in 12 burned and 12 non-burned sites in four ecological systems. The seasonal and diel biological acoustic activity were described using the “Bioacoustic Index”, a detailed aural analysis of sound sources, and a new tool called “Sonic Timelapse Builder” (STLB).Results: Seasonal biophony phenology showed a diurnal peak in June and a nocturnal peak in October in all ecological systems. On June mornings, acoustic activity was lower at burned than at non-burned sites in three of four ecological systems, due to a decreased abundance of cicadas directly impacted by the death of trees. Aural analyses revealed that 55% of recordings from non-burned sites contained insect sounds compared to 18% from burned sites. On October nights, orthopteran activity was more prevalent at some burned sites, possibly due to post-fire emergence of herbaceous.Conclusions: Soundscape approaches can help address long-term conservation issues involving the responses of animal communities to wildfire. Acoustic methods can serve as a valuable complement to remote sensing for disturbance-based landscape management. [ABSTRACT FROM AUTHOR]

Addressing serious environmental challenges, or wicked problems, locally and globally, we argue here that working collaboratively as scientist and humanist we are in a strategic position to help address biodiversity crises. We outline synergies that combine the strengths, tools, and fresh perspectives of soundscape ecology and sound studies in ethnomusicology. Our unique collaboration places sound at the core of our process but utilizes a community acoustics lens to bring both the sounds of nature and those of people together to couple our epistemologies, methodologies, and deep commitment to addressing the ecological needs today. [ABSTRACT FROM AUTHOR]

Soundscape ecology and ecoacoustics study the spatiotemporal dynamics of a soundscape and how the dynamics reflect and influence ecological processes in the environment. Soundscape analysis methods employ acoustic recording units (ARUs) that collect acoustic data in study areas over time. Analyzing these data includes computation of several acoustic diversity indices developed to quantify species abundance, richness, or habitat condition through digital audio processing and algorithm adaptations for within-group populations. However, the success of specific indices is often dependent on habitat type and biota richness present and analyzing these data can be challenging due to temporal pseudo-replication. Time-series analytical methods address the inherent problems of temporal autocorrelation for soundscape analyses challenges. Animal population dynamics fluctuate in a variety of ways due to changes in habitat or natural patterns of a landscape and chronic noise exposure, with soundscape phenology patterns evident in terrestrial and aquatic environments. Historical phenological soundscape patterns have been used to predict expected soundscape patterns in long-term studies but limited work has explored how forecasting can quantify changes in short-term studies. We evaluate how forecasting from an acoustic index can be used to quantify change in an acoustic community response to a loud, acute noise. We found that the acoustic community of a Midwestern restored prairie had decreased acoustic community activity after a loud sound event (LSE), a Civil War Reenactment, mainly driven by observed changes in the bird community and quantified using two methods: an automated acoustic index and species richness. Time-series forecasting maybe considered an underutilized tool in analyzing acoustic data whose experimental design can be flawed with temporal autocorrelation. Forecasting using auto ARIMA with acoustic indices could benefit decision makers who consider ecological questions at different time scales. • Forecasting using auto ARIMA with acoustic index quantifies changes in the acoustic community in a Midwestern restored prairie. • Short-term pre and post study indicates loud sound event is disruptive to acoustic community patterns. • Forecasting with spectral entropy index is supported by species richness curves. [ABSTRACT FROM AUTHOR]

Modern biogeography now encompasses an impressive diversity of patterns and phenomena of the geography of nature, providing insights fundamental to understanding the forces influencing the spatial and temporal dynamics of biological diversity. However, rather than praise our discipline for its great breadth of visions, our purpose here is to point out our glaring oversight of a potentially transformative frontier in the geography of nature. A new, emerging area called soundscape ecology, if guided by the principles of biogeography, holds the promise of 'opening the ears' of our field and providing fresh perspectives on fundamental problems being addressed by biogeographers. [ABSTRACT FROM AUTHOR]

Soundscape ecology is an emergent and potentially transformative scientific discipline. However, the majority of research within the field has been conducted by natural scientists focused on quantifying the characteristics and dynamics of soundscapes and examining their effect on non-human biota. A more holistic approach to the science and management of soundscapes requires full integration with the social and policy sciences. To facilitate the development of this integration, we propose an integrative human and policy dimensions of soundscape ecology framework that conceptualizes the complex and dynamic relationships between humans and their acoustic environments. The framework is grounded in four distinct disciplines – health, psychology, economics and anthropology – that have used different methodologies and metrics to focus on certain aspects of human–soundscape interactions. We provide a review of previous empirical research within each of these fields. Along the way, we identify unexplored avenues of discipline-specific research that can further the field of soundscape ecology. The human and policy dimensions of soundscape ecology framework provide the logic and structure upon which an interdisciplinary body of scholarship can be built in the future. We conclude by utilizing our review and integrative framework to propose specific focused soundscape policy and management recommendations. We argue the anthropogenic dominance of soundscapes can be mitigated through more proactive, integrative and holistic soundscape policies and management practices. [ABSTRACT FROM AUTHOR]

We summarize the foundational elements of a new area of research we call soundscape ecology. The study of sound in landscapes is based on an understanding of how sound, from various sources-biological, geophysical and anthropogenic-can be used to understand coupled natural-human dynamics across different spatial and temporal scales. Useful terms, such as soundscapes, biophony, geophony and anthrophony, are introduced and defined. The intellectual foundations of soundscape ecology are described-those of spatial ecology, bioacoustics, urban environmental acoustics and acoustic ecology. We argue that soundscape ecology differs from the humanities driven focus of acoustic ecology although soundscape ecology will likely need its rich vocabulary and conservation ethic. An integrative framework is presented that describes how climate, land transformations, biodiversity patterns, timing of life history events and human activities create the dynamic soundscape. We also summarize what is currently known about factors that control temporal soundscape dynamics and variability across spatial gradients. Several different phonic interactions (e.g., how anthrophony affects biophony) are also described. Soundscape ecology tools that will be needed are also discussed along with the several ways in which soundscapes need to be managed. This summary article helps frame the other more application-oriented papers that appear in this special issue. [ABSTRACT FROM AUTHOR]

We argue that soundscapes possess both ecological and social value and that they should be considered natural resources worthy of management and conservation. In this paper we bring together diverse bodies of literature that identify the human and ecological benefits provided by soundscapes. Sense of place, cultural significance, interactions with landscape perceptions, and wildlife wellbeing are a few of the values ascribed to soundscapes. The values and benefits of soundscapes are motivation to advance soundscape conservation and management. Given that soundscape conservation is new, we present a summary of conservation principles that need to be considered in soundscape conservation planning. These include the need to set goals, identify targets, assess condition, identify and manage threats, and conduct monitoring of the soundscape. We also argue that soundscape conservation needs to consider the soundscape within the larger mosaic of the landscape that is occupied by humans-a perspective provided by landscape ecology. We describe several different kinds of soundscapes that need to be conserved, such as natural quiet, sensitive, threatened, and unique soundscapes, and the ways that conservation planning can protect these for the future. [ABSTRACT FROM AUTHOR]

In this paper we present an introduction to the physical characteristics of sound, basic recording principles as well as several ways to analyze digital sound files using spectrogram analysis. This paper is designed to be a 'primer' which we hope will encourage landscape ecologists to study soundscapes. This primer uses data from a long-term study that are analyzed using common software tools. The paper presents these analyses as exercises. Spectrogram analyses are presented here introducing indices familiar to ecologists (e.g., Shannon's diversity, evenness, dominance) and GIS experts (patch analysis). A supplemental online tutorial provides detailed instructions with step by step directions for these exercises. We discuss specific terms when working with digital sound analysis, comment on the state of the art in acoustic analysis and present recommendations for future research. [ABSTRACT FROM AUTHOR]

• Avian diversity was assessed with point counts and passive acoustic monitoring. • Highway noise and its edge effect negatively affected diversity in certain habitats. • Effects differed between interior and edge specialist species. • Avian acoustic activity increased with distance from highways. In the 1940s, Aldo Leopold took extensive notes on birds and their sounds near his iconic shack in Baraboo, Wisconsin, USA. His observations, along with his land management techniques, helped frame his seminal book, A Sand County Almanac. After his death, two interstate highways were built near his property and subjected this historically significant area to traffic noise. While highways currently represent vital transportation corridors, their observed and potential impacts on biodiversity and ecosystem services are cause for concern. As the area including Leopold's shack is now an Important Bird Area, we sought to evaluate the impact of these highways on the bird community and its related acoustic diversity. In 2011, 150 avian point counts were conducted in the three main habitats composing the landscape—upland deciduous forest, floodplain forest, and herbaceous wetland. In 2012, soundscape recordings were collected in seven floodplain forest sites using automated passive acoustic recorders. We described the local bird communities and measured their acoustic diversity. Linear models accounting for additional factors including land cover and vegetation structure characteristics showed that as the distance from highways increased, bird community descriptors (overall abundance and species richness) and acoustic diversity increased (when relationships were significant). On the species level, forest interior specialists were negatively affected by the presence of the highways, contrary to edge specialists. In addition to the direct effects of the edges produced by the highway structure, this difference might be due to the masking effect of traffic noise on interior specialists' low-frequency vocalizations and their reliance on acoustic, as opposed to visual, communication. We conclude that while habitat structure is a principle driver of bird diversity on a broader scale, highway-induced changes in both habitat structure and soundscapes may affect bird communities. [ABSTRACT FROM AUTHOR]

• The global threshold scheme to generate the binary spectrogram makes ADI sensitive to noise. • The proposed FADI is robust to noise within a much wider SNR range as compared with ADI. • FADI has a potential to expand the spatial–temporal coverage in PAM applications. In recent years, passive acoustic monitoring (PAM) has been widely applied in the field of soundscape ecology. Meanwhile, acoustic indices, as an important tool for rapid biodiversity assessment (RBA), have also attracted increasing attention. However, considerable evidence demonstrates that existing acoustic indices are susceptible to noise, which poses a challenge for wider applications of acoustic indices. In this work, we firstly provide insights into how noise affects the widely used acoustic diversity index (ADI), revealing that the global threshold scheme (i.e., −50 dBFS for all time–frequency bins) to generate the binary spectrogram (spectrogram containing time–frequency bins denoted as '1′ with power above the threshold and '0′ below) is the key factor that makes ADI sensitive to noise. To address this problem, we propose a revised ADI version named frequency-dependent acoustic diversity index (FADI), which employs a new threshold scheme to reduce noise impacts on its values. Specifically, considering that the noise floor along frequency axis usually varies in practice, i.e., a colored noise rather than a white noise exists in field recordings, floating thresholds adapted to the noise level at each frequency bin were employed and further incorporated with the global threshold of ADI. Controlled computational experiments using field recordings were conducted where manipulated noise levels were introduced as different signal-to-noise ratios (SNR). It was shown that the proposed FADI was significantly robust to noise within a much wider SNR range as compared with ADI. Furthermore, a singular example using a real-world recording also verified its effectiveness and highlighted the promising potential of FADI in applications of PAM. [ABSTRACT FROM AUTHOR]