Comparing manual and automated species recognition in the detection of four common south-east Australian forest birds from digital field recordings

Joshi, Kara A.; Mulder, Raoul A.; Rowe, Karen M. C.

doi:10.1080/01584197.2017.1298970

Cited by 25 publications

(28 citation statements)

References 62 publications

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“…Nevertheless, more comparative work is required before the costeffectiveness of these tools can be determined. Joshi et al (2017) showed that such software is cost-effective in some situations but not others. Automatic detection of bittern calls has so far not been found to be cost-effective, either because expensive recording equipment was needed (Frommolt & Tauchert, 2014) or because it gave high false positive rates (21% precision rate; Priyadarshani, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, there are currently several options for processing recordings. Sound files can be: (a) manually processed by listening or visually examining files for evidence of calls on spectrograms; or (b) automatically processed using software trained to identify unique sound shapes associated with calls of the target species (Brandes, 2008;Joshi, Mulder, & Rowe, 2017). Here, we tested four possible manual options for monitoring wildlife calls using recording devices: (a) stereo recordings processed visually (STEREO-VISUAL), (b) mono recordings processed visually (MONO-VISUAL), (c) stereo recordings processed audibly (STEREO-AUDIBLE), and (d) mono recordings processed audibly (MONO-AUDIBLE).…”

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confidence: 99%

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Cost‐benefit analysis of acoustic recorders as a solution to sampling challenges experienced monitoring cryptic species

Williams

O’Donnell²,

Armstrong

2018

Ecology and Evolution

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The inferences that can be made from any study are limited by the quality of the sampling design. By bad luck, when monitoring species that are difficult to detect (cryptic), sampling designs become dictated by what is feasible rather than what is desired. We calibrated and conducted a cost‐benefit analysis of four acoustic recorder options that were being considered as potential solutions to several sampling restrictions experienced while monitoring the Australasian bittern, a cryptic wetland bird. Such sampling restrictions are commonly experienced while monitoring many different endangered species, particularly those that are cryptic. The recorder options included mono and stereo devices, with two sound file processing options (visual and audible analysis). Recording devices provided call‐count data similar to those collected by field observers but at a fraction of the cost, which meant that “idealistic” sampling regimes, previously thought to be too expensive, became feasible for bitterns. Our study is one of the few to assess the monetary value of recording devices in the context of data quality, allowing trade‐offs (and potential solutions) commonly experienced while monitoring cryptic endangered species to be shown and compared more clearly. The ability to overcome challenges of monitoring cryptic species in this way increases research possibilities for data deficient species and is applicable to any species with similar monitoring challenges.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Cost‐benefit analysis of acoustic recorders as a solution to sampling challenges experienced monitoring cryptic species

Williams

O’Donnell²,

Armstrong

2018

Ecology and Evolution

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“…, Joshi et al. ). Nevertheless, rapid progress is being made and it is currently possible to rely only on the vocalisations contained within the field recordings to generate classifiers (Ovaskainen et al.…”

Section: Practicalitymentioning

confidence: 99%

“…However, the recordings used for benchmarking are sometimes not representative of realworld, noisier conditions (Priyadarshani et al 2018). The efficiency of automated species detection methods also depends on the method used, the quality of the recordings, and the target species: efficiency compared to manual processing is sometimes equivalent or lower (Digby et al 2013, Joshi et al 2017. Nevertheless, rapid progress is being made and it is currently possible to rely only on the vocalisations contained within the field recordings to generate classifiers (Ovaskainen et al 2018).…”

Section: Practicalitymentioning

confidence: 99%

Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide

Darras

Batáry

Furnas

et al. 2019

Ecological Applications

118

100

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Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small‐scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data‐driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.

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“…All recognizers misclassify to some extent (Priyadarshani et al 2018), which can have implications for study results (Russo and Voigt 2016, Rydell et al 2017). Typically, each detection reported by a recognizer is visually or aurally reviewed by a human observer (hereafter “manual validation”) to remove false positives; however, the time required for validation can render automated recognition no more efficient than processing recordings aurally (Borker et al 2014, Joshi et al 2017). Thus, there is a need for methods that increase the automation of acoustic recognition (Marques et al 2012, Stowell et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Validation prediction: a flexible protocol to increase efficiency of automated acoustic processing for wildlife research

Knight

Sólymos

Scott³

et al. 2020

Ecological Applications

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Automated recognition is increasingly used to extract species detections from audio recordings; however, the time required to manually review each detection can be prohibitive. We developed a flexible protocol called "validation prediction" that uses machine learning to predict whether recognizer detections are true or false positives and can be applied to any recognizer type, ecological application, or analytical approach. Validation prediction uses a predictable relationship between recognizer score and the energy of an acoustic signal but can also incorporate any other ecological or spectral predictors (e.g., time of day, dominant frequency) that will help separate true from false-positive recognizer detections. First, we documented the relationship between recognizer score and the energy of an acoustic signal for two different recognizer algorithm types (hidden Markov models and convolutional neural networks). Next, we demonstrated our protocol using a case study of two species, the Common Nighthawk (Chordeiles minor) and Ovenbird (Seiurus aurocapilla). We reduced the number of detections that required validation by 75.7% and 42.9%, respectively, while retaining at least 98% of the true-positive detections. Validation prediction substantially improves the efficiency of using automated recognition on acoustic data sets. Our method can be of use to wildlife monitoring and research programs and will facilitate using automated recognition to mine bioacoustic data sets.

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Comparing manual and automated species recognition in the detection of four common south-east Australian forest birds from digital field recordings

Cited by 25 publications

References 62 publications

Cost‐benefit analysis of acoustic recorders as a solution to sampling challenges experienced monitoring cryptic species

Cost‐benefit analysis of acoustic recorders as a solution to sampling challenges experienced monitoring cryptic species

Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide

Validation prediction: a flexible protocol to increase efficiency of automated acoustic processing for wildlife research

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