[go: up one dir, main page]

Fairbrass et al., 2019 - Google Patents

CityNet—Deep learning tools for urban ecoacoustic assessment

Fairbrass et al., 2019

View PDF @Full View
Document ID
13300986854470552636
Author
Fairbrass A
Firman M
Williams C
Brostow G
Titheridge H
Jones K
Publication year
Publication venue
Methods in ecology and evolution

External Links

Snippet

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 …
Continue reading at besjournals.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management

Similar Documents

Publication Publication Date Title
Fairbrass et al. CityNet—Deep learning tools for urban ecoacoustic assessment
Gibb et al. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring
Metcalf et al. Acoustic indices perform better when applied at ecologically meaningful time and frequency scales
Mac Aodha et al. Bat detective—Deep learning tools for bat acoustic signal detection
Stowell et al. Automatic acoustic detection of birds through deep learning: the first bird audio detection challenge
Ulloa et al. scikit‐maad: an open‐source and modular toolbox for quantitative soundscape analysis in Python
Heinicke et al. Assessing the performance of a semi‐automated acoustic monitoring system for primates
Darras et al. Comparing the sampling performance of sound recorders versus point counts in bird surveys: A meta‐analysis
Brodie et al. Automated species identification of frog choruses in environmental recordings using acoustic indices
Knight et al. Pre-processing spectrogram parameters improve the accuracy of bioacoustic classification using convolutional neural networks
Longden et al. Mark–recapture of individually distinctive calls—a case study with signature whistles of bottlenose dolphins (Tursiops truncatus)
Brooker et al. Automated detection and classification of birdsong: An ensemble approach
Bergler et al. ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning
Miller et al. An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors
Kitzes et al. Large roads reduce bat activity across multiple species
Katsis et al. Automated detection of gunshots in tropical forests using convolutional neural networks
Marchal et al. Software performance for the automated identification of bird vocalisations: the case of two closely related species
Knight et al. Validation prediction: a flexible protocol to increase efficiency of automated acoustic processing for wildlife research
Singer et al. Aggregated time‐series features boost species‐specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages
Yip et al. Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
Diaz et al. Acoustic indices as proxies for bird species richness in an urban green space in Metro Manila
Aodha et al. Towards a general approach for bat echolocation detection and classification
Khalighifar et al. NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations
Lostanlen et al. BirdVoxDetect: Large-scale detection and classification of flight calls for bird migration monitoring
Brinkløv et al. Open‐source workflow approaches to passive acoustic monitoring of bats