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The use of acoustic indices to determine avian species richness in audio-recordings of the environment

Ecological informatics, 2014-05, Vol.21, p.110-119

2014

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Sampling environmental acoustic recordings to determine bird species richness

Ecological applications, 2013-09, Vol.23 (6), p.1419-1428

2013

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Revealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation

PloS one, 2018-03, Vol.13 (3), p.e0193345-e0193345

2018

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A new method to estimate abundance of Australasian Bittern ( Botaurus poiciloptilus ) from acoustic recordings

Avian conservation and ecology, 2024-06, Vol.19 (1), p.16, Article art16

2024

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A practical comparison of manual and autonomous methods for acoustic monitoring

Methods in ecology and evolution, 2013-07, Vol.4 (7), p.675-683

2013

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Assessing the potential of acoustic indices for protected area monitoring in the Serra do Cipó National Park, Brazil

Ecological indicators, 2021-01, Vol.120, p.106953, Article 106953

2021

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Using a Novel Visualization Tool for Rapid Survey of Long-Duration Acoustic Recordings for Ecological Studies of Frog Chorusing

Frontiers in ecology and evolution, 2022-01, Vol.9

2022

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The Emergence of Subtle Organicism

Journal of Futures Studies, 2011-09, Vol.16 (1), p.109-136

Michael Towsey

2011

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The Emergence of Subtle Organicism A Reply to Comments from M. Bussey, T. Dolan, W. Halal and T. Lombardo

Journal of Futures Studies, 2011-09, Vol.16 (1), p.169-176

Michael Towsey

2011

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Automated species identification of frog choruses in environmental recordings using acoustic indices

Ecological indicators, 2020-12, Vol.119, p.106852, Article 106852

2020

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Acoustic monitoring reveals year-round calling by invasive toads in tropical Australia

Bioacoustics (Berkhamsted), 2021-03, Vol.30 (2), p.125-141

2021

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An intelligent system for estimating frog community calling activity and species richness

Ecological indicators, 2017-11, Vol.82, p.13-22

2017

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Frog call classification: a survey

The Artificial intelligence review, 2018-03, Vol.49 (3), p.375-391

2018

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Analysing environmental acoustic data through collaboration and automation

Future generation computer systems, 2013-02, Vol.29 (2), p.560-568

2013

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Visualization of Long-duration Acoustic Recordings of the Environment

Procedia computer science, 2014, Vol.29, p.703-712

2014

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A novel frog chorusing recognition method with acoustic indices and machine learning

Future generation computer systems, 2021-12, Vol.125, p.485-495

2021

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Using soundscapes to investigate homogenization of tropical forest diversity in selectively logged forests

The Journal of applied ecology, 2019-11, Vol.56 (11), p.2493-2504

2019

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Using visualization and machine learning methods to monitor low detectability species—The least bittern as a case study

Ecological informatics, 2020-01, Vol.55 (C), p.101014, Article 101014

2020

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Using non-negative matrix factorisation to facilitate efficient bird species richness surveys

Ecological indicators, 2017-09, Vol.80, p.297-302

2017

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Acoustic region workflow for efficient comparison of soundscapes under different invasive mammals' management regimes

Ecological informatics, 2022-05, Vol.68, p.101554, Article 101554

2022

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Ecology and acoustics at a large scale

Ecological informatics, 2014-05, Vol.21, p.1-3

2014

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A survey of tagging techniques for music, speech and environmental sound

The Artificial intelligence review, 2014-12, Vol.42 (4), p.637-661

2014

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Data selection in frog chorusing recognition with acoustic indices

Ecological informatics, 2020-11, Vol.60, p.101160, Article 101160

2020

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Active learning for classifying long‐duration audio recordings of the environment

Methods in ecology and evolution, 2018-09, Vol.9 (9), p.1948-1958

2018

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The prediction of bacterial transcription start sites using SVMs

International journal of neural systems, 2006-10, Vol.16 (5), p.363-370

2006

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The use of acoustic indices to determine avian species richness in audio-recordings of the environment

Sampling environmental acoustic recordings to determine bird species richness

Revealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation

A new method to estimate abundance of Australasian Bittern ( Botaurus poiciloptilus ) from acoustic recordings

A practical comparison of manual and autonomous methods for acoustic monitoring

Assessing the potential of acoustic indices for protected area monitoring in the Serra do Cipó National Park, Brazil

Using a Novel Visualization Tool for Rapid Survey of Long-Duration Acoustic Recordings for Ecological Studies of Frog Chorusing

The Emergence of Subtle Organicism

The Emergence of Subtle Organicism A Reply to Comments from M. Bussey, T. Dolan, W. Halal and T. Lombardo

Automated species identification of frog choruses in environmental recordings using acoustic indices

Acoustic monitoring reveals year-round calling by invasive toads in tropical Australia

An intelligent system for estimating frog community calling activity and species richness

Frog call classification: a survey

Analysing environmental acoustic data through collaboration and automation

Visualization of Long-duration Acoustic Recordings of the Environment

A novel frog chorusing recognition method with acoustic indices and machine learning

Using soundscapes to investigate homogenization of tropical forest diversity in selectively logged forests

Using visualization and machine learning methods to monitor low detectability species—The least bittern as a case study

Using non-negative matrix factorisation to facilitate efficient bird species richness surveys

Acoustic region workflow for efficient comparison of soundscapes under different invasive mammals' management regimes

Ecology and acoustics at a large scale

A survey of tagging techniques for music, speech and environmental sound

Data selection in frog chorusing recognition with acoustic indices

Active learning for classifying long‐duration audio recordings of the environment

The prediction of bacterial transcription start sites using SVMs

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