Tools for automated acoustic monitoring within the R package monitoR

Katz, Jonathan E.; Hafner, Sasha D.; Donovan, Therese M.

doi:10.1080/09524622.2016.1138415

Cited by 68 publications

(83 citation statements)

References 18 publications

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“…For conservation programs aiming to detect rare or cryptic species or behaviors, specificity should be low. An example of this is the new template-matching R package monitoR (Hafner & Katz, 2017;Katz, Hafner, & Donovan, 2016) which allows the detection threshold (specificity) to be set by the end-user. Conversely, for programs in which detecting every call is not necessary, or where the cessation of a vocal behavior is of interest (e.g., to indicate the end of breeding), then reducing false positives by increasing specificity will be more important.…”

Section: Challenges and Considerations For Bioacoustic Monitoring Pmentioning

confidence: 99%

Bioacoustic monitoring of animal vocal behavior for conservation

Teixeira

Maron

Rensburg

2019

Conservat Sci and Prac

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The popularity of bioacoustics for threatened species monitoring has surged. Large volumes of acoustic data can be collected autonomously and remotely with minimal human effort. The approach is commonly used to detect cryptic species and, more recently, to estimate abundance or density. However, the potential for conservationrelevant information to be derived from acoustic signatures associated with particular behavior is less well-exploited. Animal vocal behavior can reveal important information about critical life history events. In this study, we argue that the overlap of the disciplines of bioacoustics, vocal communication, and conservation behavior-thus, "acoustic conservation behavior"-has much to offer threatened species monitoring.In particular, vocalizations can serve as indicators of behavioral states and contexts that provide insight into populations as it relates to their conservation. We explore the information available from monitoring species' vocalizations that relate to reproduction and recruitment, alarm and defense, and social behavior, and how this information could translate into potential conservation benefits. While there are still challenges to processing acoustic data, we conclude that acoustic conservation behavior may improve threatened species monitoring where vocalizations reveal behaviors that are informative for management and decision-making. K E Y W O R D Sbioacoustics, conservation behavior, monitoring, vocal behavior, vocalizations

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Section: Challenges and Considerations For Bioacoustic Monitoring Pmentioning

confidence: 99%

Bioacoustic monitoring of animal vocal behavior for conservation

Teixeira

Maron

Rensburg

2019

Conservat Sci and Prac

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“…The "soundecology" package has functions to implement acoustic indices to characterize animal acoustic communities and soundscapes from the physical attributes of sound on recordings (Villanueva-Rivera and Pijanowski 2016). In addition to automated species recognition, the "monitoR" package has functions to rename recordings and isolate shorter segments in long recordings, which can be useful if using an ARU with limited scheduling capabilities (Katz et al 2016). The recently developed "warbleR" package builds upon "seewave" and "monitoR" functions to streamline analyses of acoustic signal structure by measuring signal parameters (frequency, time, and amplitude) and pairwise acoustic dissimilarity and performing pairwise spectrogram cross-correlations (Araya-Salas and Smith-Vidaurre 2017).…”

Section: Techniques For Processing Recordingsmentioning

confidence: 99%

“…In general, the process involves matching recording segments to a template (often termed a "recognizer") derived from training data and registering a hit when a similarity threshold is reached. A few different approaches have been developed, including band-limited energy detectors (Mills 2000), binary point matching (Katz et al 2016), decision trees (Acevedo et al 2009, Digby et al 2013, random forests (Ross and Allen 2014), spectrogram cross-correlation (Katz et al 2016), hidden Markov models (Wildlife Acoustics 2011), and, most recently, deep learning through convolutional neural networks (Salamon and Bello 2017). Only a few of these approaches are incorporated into commercial or open-source software, including Song Scope (Wildlife Acoustics, Maynard, Massachusetts, USA), Raven Pro (Cornell Laboratory of Ornithology, Ithaca, New York, USA), and R package "monitoR" (Hafner and Katz 2017), making them more easily accessible to avian researchers.…”

Section: Techniques For Processing Recordingsmentioning

confidence: 99%

Autonomous recording units in avian ecological research: current use and future applications

Shonfield¹,

Bayne²

2017

ACE

239

241

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ABSTRACT. Acoustic surveys are a widely used sampling tool in ecological research and monitoring. They are used to monitor populations and ecosystems and to study various aspects of animal behavior. Autonomous recording units (ARUs) can record sound in most environments and are increasingly used by researchers to conduct acoustic surveys for birds. In this review, we summarize the use of ARUs in avian ecological research and synthesize current knowledge of the benefits and drawbacks of this technology. ARUs enable researchers to do more repeat visits with less time spent in the field, with the added benefits of a permanent record of the data collected and reduced observer bias. They are useful in remote locations and for targeting rare species. ARUs are mostly comparable to human observers in terms of species richness, but in some cases, they detect fewer species and at shorter distances. Drawbacks of ARUs include the cost of equipment, storage of recordings, loss of data if units fail, and potential sampling trade-offs in spatial vs. temporal coverage. ARUs generate large data sets of audio recordings, but advances in automated species recognition and acoustic processing techniques are contributing to make the processing time manageable. Future applications of ARUs include biodiversity monitoring and studying habitat use, animal movement, and various behavioral ecology questions based on vocalization activity. ARUs have the potential to make significant advances in avian ecological research and to be used in more innovative ways than simply as a substitute for a human observer in the field. Unités d'enregistrement autonomes en écologie aviaire : utilisations actuelles et futuresRÉSUMÉ. Les relevés acoustiques sont largement employés comme outil d'échantillonnage pour la recherche et les suivis écologiques. Ils sont utilisés pour suivre les populations et les écosystèmes et étudier le comportement animal sous divers angles. Les unités d'enregistrement autonomes (ARU, pour autonomus recording units) peuvent enregistrer les sons dans la plupart des environnements et sont utilisés de plus en plus souvent par les chercheurs pour effectuer des relevés acoustiques d'oiseaux. Dans la présente revue, nous résumons l'utilisation d'ARU en écologie aviaire et synthétisons les avantages et désavantages connus de cette technologie. Les ARU permettent aux chercheurs de faire plus de visites répétées tout en passant moins de temps sur le terrain, avec l'avantage d'avoir un enregistrement permanent des données récoltées et de réduire le biais associé à l'observateur. Ils sont utiles en milieu éloigné et dans les cas où on cible des espèces rares. Les ARU sont comparables aux observateurs humains pour ce qui est de la richesse spécifique, mais dans certains cas, ils détectent moins d'espèces et à des distances plus courtes. Les désavantages des ARU comprennent le coût de l'équipement, la sauvegarde des enregistrements, la perte de données en cas de défaillance des unités, et le compromis possible à devoir faire sur le plan de la...

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“…Alternative algorithms have been developed and tested for the automated recognition of bird songs from continuous recordings (Kogan and Margoliash 1998, Acevedo et al 2009, Katz et al 2016 and it is likely that there will be continued progress on the development of new and improved algorithms over time, including multispecies approaches (Briggs et al 2012). However, results from our study suggest that current algorithms are effective for many existing applications when sufficient recording data is assessed, and can, in broad context, enhance our collective ability to achieve both conservation and sustainable forest management goals when used appropriately.…”

Section: Discussionmentioning

confidence: 99%

“…Automated recognition of bird songs is currently a very active area of research (Kogan and Margoliash 1998, Briggs et al 2012, Potamitis et al 2014, de Oliveira et al 2015, Katz et al 2016 (Katz et al 2016). In this study, we employed Song Scope software that features an algorithm based on Hidden Markov Models (HMM) devised to evaluate spectral and temporal features of individual syllables as well as how syllables are structured to form complex songs (Kogan andMargoliash 1998, Somervuo et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Comparison of semiautomated bird song recognition with manual detection of recorded bird song samples

Venier¹,

Mazerolle²,

Rodgers³

et al. 2017

ACE

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ABSTRACT. Automated recording units are increasingly being used to sample wildlife populations. These devices can produce large amounts of data that are difficult to process manually. However, the information in the recordings can be summarized with semiautomated sound recognition software. Our objective was to assess the utility of the semiautomated bird song recognizers to produce data useful for conservation and sustainable forest management applications. We compared detection data generated from expert-interpreted recordings of bird songs collected with automated recording units and data derived from a semiautomated recognition process. We recorded bird songs at 109 sites in boreal forest in 2013 and 2014 using automated recording units. We developed bird-song recognizers for 10 species using Song Scope software (Wildlife Acoustics) and each recognizer was used to scan a set of recordings that was also interpreted manually by an expert in birdsong identification. We used occupancy models to estimate the detection probability associated with each method. Based on these detection probability estimates we produced cumulative detection probability curves. In a second analysis we estimated detection probability of bird song recognizers using multiple 10-minute recordings for a single station and visit (35-63, 10-minute recordings in each of four one-week periods). Results show that the detection probability of most species from single 10-min recordings is substantially higher using expert-interpreted bird song recordings than using the song recognizer software. However, our results also indicate that detection probabilities for song recognizers can be significantly improved by using more than a single 10-minute recording, which can be easily done with little additional cost with the automate procedure. Based on these results we suggest that automated recording units and song recognizer software can be valuable tools to estimate detection probability and occupancy of boreal forest birds, when sampling for sufficiently long periods. Comparaison de la reconnaissance semi-automatisée de chants d'oiseaux avec des détections manuelles d'échantillons de chants d'oiseaux enregistrésRÉSUMÉ. Les unités d'enregistrement automatisé sont de plus en plus utilisées pour échantillonner les populations fauniques. Ces instruments peuvent produire une grande quantité de données qui s'avèrent difficiles à traiter manuellement. Toutefois, les informations contenues sur les enregistrements peuvent être résumées à l'aide de logiciels de reconnaissance vocale semi-automatisée. L'objectif de notre étude était d'évaluer l'utilité des reconnaisseurs de chants d'oiseaux semi-automatisés pour produire des données utiles à la conservation et à l'application de mesures d'aménagement forestier durable. Nous avons comparé les données de détection générées par les experts ayant écouté les enregistrements de chants d'oiseaux collectés au moyen d'unités d'enregistrement automatisé avec les données obtenues au moyen d'un processus de reconnaissance ...

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Tools for automated acoustic monitoring within the R package monitoR

Cited by 68 publications

References 18 publications

Bioacoustic monitoring of animal vocal behavior for conservation

Bioacoustic monitoring of animal vocal behavior for conservation

Autonomous recording units in avian ecological research: current use and future applications

Comparison of semiautomated bird song recognition with manual detection of recorded bird song samples

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