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Comic Spin: A Comic Creation Tool Enabling Self-Expression for People with Aphasia
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Modelling Group Dynamics with SYMLOG and Snowdrift for Intelligent Classroom Environment
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“Just Not Together”: The Experience of Videoconferencing for People with Aphasia during the Covid-19 Pandemic
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A Framework for Quality Assessment of Semantic Annotations of Tabular Data
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Delivering group support for people with aphasia in a virtual world: experiences of service providers
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Anti-transfer learning for task invariance in convolutional neural networks for speech processing
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| 11 |
Comparing the environmental impacts of recipes from four different recipe databases using Natural Language Processing
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| 12 |
A World Full of Stereotypes? Further Investigation on Origin and Gender Bias in Multi-Lingual Word Embeddings ...
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Calculating the optimal step of arc-eager parsing for non-projective trees
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| 14 |
Few-shot linguistic grounding of visual attributes and relations using gaussian kernels
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| 15 |
Automated detection of Hainan gibbon calls for passive acoustic monitoring
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Dufourq, Emmanuel; Durbach, Ian; Hansford, James P.; Hoepfner, Amanda; Ma, Heidi; Bryant, Jessica V.; Stender, Christina S.; Li, Wenyong; Liu, Zhiwei; Chen, Qing; Zhou, Zhaoli; Turvey, Samuel T.. - 2021
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Abstract:
Fieldwork was funded by an Arcus Foundation grant to STT and a Wildlife Acoustics grant to JVB. ID is supported in part by funding from the National Research Foundation of South Africa (Grant ID 90782, 105782). ED is supported by a postdoctoral fellowship from the African Institute for Mathematical Sciences South Africa, Stellenbosch University and the Next Einstein Initiative. This work was carried out with the aid of a grant from the International Development Research Centre, Ottawa, Canada (www.idrc.ca), and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC; www.international.gc.ca). ; Extracting species calls from passive acoustic recordings is a common preliminary step to ecological analysis. For many species, particularly those occupying noisy, acoustically variable habitats, the call extraction process continues to be largely manual, a time-consuming and increasingly unsustainable process. Deep neural networks have been shown to offer excellent performance across a range of acoustic classification applications, but are relatively underused in ecology. We describe the steps involved in developing an automated classifier for a passive acoustic monitoring project, using the identification of calls of the Hainan gibbon Nomascus hainanus, one of the world's rarest mammal species, as a case study. This includes preprocessing-selecting a temporal resolution, windowing and annotation; data augmentation; processing-choosing and fitting appropriate neural network models; and post-processing-linking model predictions to replace, or more likely facilitate, manual labelling. Our best model converted acoustic recordings into spectrogram images on the mel frequency scale, using these to train a convolutional neural network. Model predictions were highly accurate, with per-second false positive and false negative rates of 1.5% and 22.3%. Nearly all false negatives were at the fringes of calls, adjacent to segments where the call was correctly identified, so that very few calls were missed altogether. A post-processing step identifying intervals of repeated calling reduced an 8-h recording to, on average, 22 min for manual processing, and did not miss any calling bouts over 72 h of test recordings. Gibbon calling bouts were detected regularly in multi-month recordings from all selected survey points within Bawangling National Nature Reserve, Hainan. We demonstrate that passive acoustic monitoring incorporating an automated classifier represents an effective tool for remote detection of one of the world's rarest and most threatened species. Our study highlights the viability of using neural networks to automate or greatly assist the manual labelling of data collected by passive acoustic monitoring projects. We emphasize that model development and implementation be informed and guided by ecological objectives, and increase accessibility of these tools with a series of notebooks that allow users to build and deploy their own acoustic classifiers. ; Publisher PDF ; Peer reviewed
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Keyword:
Bioacoustics; Convolutional neural networks; DAS; Deep learning; Hainan gibbons; Passive acoustic monitoring; QA75; QA75 Electronic computers. Computer science; QH301; QH301 Biology; Species identification
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URL: http://hdl.handle.net/10023/23004
https://doi.org/10.1002/rse2.201
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Exploring the characteristics of abusive behaviour in online social media settings
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Deep Scattering and End-to-End Speech Models towards Low Resource Speech Recognition
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| 18 |
Framework for Composition of Domain Specific Languages and the Effect of Composition on Re-use of Translation Rules
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| 19 |
Superstar to Superhuman: Scarlett Johansson, an ‘Ideal’ Embodiment of the Posthuman Female in Science Fiction and Media?
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Qualitative-geometric ‘surrounds’ relations between disjoint regions
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