Two convolutional neural networks for bird detection in audio signals

Grill, Thomas; Schlüter, Jan

doi:10.23919/eusipco.2017.8081512

Cited by 69 publications

(61 citation statements)

References 4 publications

(9 reference statements)

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“…The precision and recall of CityBioNet was also compared to bulbul (Grill & Schlüter, ), an algorithm for detecting bird sounds in entire audio recordings in order to summarise avian acoustic activity which was the winning entry in the 2016–2017 Bird Audio Detection challenge (Stowell, Wood, Stylianou, & Glotin, ). Like CityNet, bulbul is a CNN‐based classifier which uses spectrograms as input.…”

Section: Methodsmentioning

confidence: 99%

“…Rather than compare CityNet to the NDSI, we compared the biotic (NDSI bio ) and anthropogenic (NDSI anthro ) elements of the NDSI to the measures produced by CityBioNet and CityAnthroNet, respectively, as these were more comparable. As the Acoustic Indices are all designed to give a summary of acoustic activity for an entire file, they were analysed on the The precision and recall of CityBioNet was also compared to bulbul (Grill & Schlüter, 2017), an algorithm for detecting bird sounds in entire audio recordings in order to summarise avian acoustic activity which was the winning entry in the 2016-2017 Bird Audio Detection challenge (Stowell, Wood, Stylianou, & Glotin, 2016). Like CityNet, bulbul is a CNN-based classifier which uses spectrograms as input.…”

Section: Competing Algorithmsmentioning

confidence: 99%

“…Each CNN predicted the presence or absence of each sound type at each moment in time, and these per‐time predictions were aggregated to provide a measure of acoustic activity. We compared the performance of CityNet using a held‐out dataset by comparing the algorithms’ precision and recall to four commonly used Acoustic Indices: Acoustic Complexity Index (ACI) (Pieretti et al., ), Acoustic Diversity Index (ADI) (Villanueva‐Rivera et al., ), Bioacoustic Index (BI) (Boelman et al., ), Normalised Difference Soundscape Index (NDSI) (Kasten et al., ), and to bulbul, a state‐of‐the‐art algorithm for detecting bird sounds in order to summarise avian acoustic activity (Grill & Schlüter, ). As the main focus of the study was the development of algorithms for ecoacoustic assessment of biodiversity in cities, we conducted further analysis on the two best performing algorithms for measuring biotic sound, CityBioNet and bulbul, by investigating the effect of non‐biotic sounds on the accuracy of the algorithms.…”

Section: Introductionmentioning

confidence: 99%

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CityNet—Deep learning tools for urban ecoacoustic assessment

et al. 2018

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Cities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for population abundance and/or activity. However, existing algorithms systematically over and underestimate measures of biotic activity in the presence of typical urban non‐biotic sounds in recordings. We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held‐out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four Acoustic Indices: Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state‐of‐the‐art bird call detection CNN (bulbul). We also compare the effect of non‐biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient. CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, whereas the Acoustic Indices performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real‐world urban audio data. Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large‐scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pretrained models are made freely available in combination with two expert‐annotated urban audio datasets to facilitate automated environmental surveillance in cities.

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Section: Methodsmentioning

confidence: 99%

Section: Competing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CityNet—Deep learning tools for urban ecoacoustic assessment

et al. 2018

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“…For example, Acoustic Indices (AIs) use the spectral and temporal characteristics of acoustic 78 energy in sound recordings to produce whole community measures of biotic and 79 anthropogenic sound (Sueur et al 2014). However, several commonly used AIs have been 80 shown to be biased by non-biotic sounds (Towsey et al 2014 (Kasten 122 et al 2012), and to bulbul, a state-of-the-art algorithm for detecting bird sounds in order to 123 summarise avian acoustic activity (Grill & Schlüter 2017). As the main focus of the study 124 was the development of algorithms for ecoacoustic assessment of biodiversity in cities, we 125 conducted further analysis on the two best performing algorithms for measuring biotic sound, 126 CityBioNet and bulbul, by investigating the effect of non-biotic sounds on the accuracy of the 127 algorithms.…”

mentioning

confidence: 99%

“…The precision and recall of CityBioNet was also compared to bulbul (Grill & Schlüter 2017), 249 an algorithm for detecting bird sounds in entire audio recordings in order to summarise avian 250 acoustic activity which was the winning entry in the 2016-7 Bird Audio Detection challenge 251 (Stowell et al 2016). Like CityNet, bulbul is a CNN-based classifier which uses 252 spectrograms as input.…”

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confidence: 99%

CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment

Fairbrass

Firman

Williams

et al. 2018

Preprint

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SUMMARYCities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for biodiversity. However, existing algorithms for measuring biotic sound have been shown to be biased by non-biotic sounds in recordings, typical of urban environments.We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held-out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four acoustic indices (AIs): Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state-of-the-art bird call detection CNN (bulbul). We also compare the effect of non-biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient.CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, while the AIs performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real-world urban audio data.Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large-scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pre-trained models are made freely available in combination with two expert-annotated urban audio datasets to facilitate automated environmental surveillance in cities.

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An integrated passive acoustic monitoring and deep learning pipeline for black‐and‐white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

Batist,

Dufourq,

Jeantet

et al. 2024

American J Primatol

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The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black‐and‐white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar‐shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in‐person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May–July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57‐h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in‐person observations, saving time, money, and labor while also providing re‐analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open‐sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.

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Two convolutional neural networks for bird detection in audio signals

Cited by 69 publications

References 4 publications

CityNet—Deep learning tools for urban ecoacoustic assessment

CityNet—Deep learning tools for urban ecoacoustic assessment

CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment

An integrated passive acoustic monitoring and deep learning pipeline for black‐and‐white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

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