The Experts below are selected from a list of 4812 Experts worldwide ranked by ideXlab platform

Harold K. Figueroa - One of the best experts on this subject based on the ideXlab platform.

  • Integrating computer‐assistance and human‐review to build richly annotated Sound Libraries.
    Journal of the Acoustical Society of America, 2010
    Co-Authors: Harold K. Figueroa
    Abstract:

    A current challenge to the effective application of bioacoustic survey methods is the curation and sharing of large collections of Sound and metadata, and their integration into a data‐analysis workflow that includes computer‐assistance and human‐review. Computer‐assistance affords the annotation of ever larger amounts of data and human‐review provides for sufficient quality of annotation and creates a feedback mechanism that supports continuous learning through an ever‐expanding collection of training data. I will present some solutions to the above challenges that are being implemented as part of the Bioacoustic Resource Network (BARN) project. BARN provides a web‐based interface to annotated Sound collections with a back‐end computational engine based on XBAT. The solutions provided by the BARN platform contain both technical and social elements. In the technical arena, we consider problems of data‐modeling for extensible annotation; strategies for the unique identification of data, derived annotations...

  • integrating computer assistance and human review to build richly annotated Sound Libraries
    Journal of the Acoustical Society of America, 2010
    Co-Authors: Harold K. Figueroa
    Abstract:

    A current challenge to the effective application of bioacoustic survey methods is the curation and sharing of large collections of Sound and metadata, and their integration into a data‐analysis workflow that includes computer‐assistance and human‐review. Computer‐assistance affords the annotation of ever larger amounts of data and human‐review provides for sufficient quality of annotation and creates a feedback mechanism that supports continuous learning through an ever‐expanding collection of training data. I will present some solutions to the above challenges that are being implemented as part of the Bioacoustic Resource Network (BARN) project. BARN provides a web‐based interface to annotated Sound collections with a back‐end computational engine based on XBAT. The solutions provided by the BARN platform contain both technical and social elements. In the technical arena, we consider problems of data‐modeling for extensible annotation; strategies for the unique identification of data, derived annotations...

Klaus Riede - One of the best experts on this subject based on the ideXlab platform.

  • Acoustic profiling of Orthoptera: present state and future needs
    Journal of Orthoptera Research, 2018
    Co-Authors: Klaus Riede
    Abstract:

    Bioacoustic monitoring and classification of animal communication signals has developed into a powerful tool for measuring and monitoring species diversity within complex communities and habitats. The high number of stridulating species among Orthoptera allows their detection and classification in a non-invasive and economic way, particularly in habitats where visual observations are difficult or even impossible, such as tropical rainforests. Major Sound archives were queried for Orthoptera songs, with special emphasis on usability as reference training Libraries for computer algorithms. Orthoptera songs are highly stereotyped, reliable taxonomic features. However, exploitation of songs for acoustic profiling is limited by the small number of reference recordings: existing song Libraries represent only about 1000 species, mainly from Europe and North America, covering less than 10% of extant stridulating Orthoptera species. Available databases are fragmented and lack tools for song annotation and efficient feature-based searching. Results from recent bioacoustic surveys illustrate the potential of the method, but also the challenges and bottlenecks impeding further progress. A major problem is time-consuming data analysis of recordings. Computer-aided identification software exists for classification and identification of cricket and grasshopper songs, but these tools are still far from practical for field application.A framework for acoustic profiling of Orthoptera should consist of the following components: (1) Protocols for standardized acoustic sampling, at species and community levels, using acoustic data loggers for autonomous long-term recordings; (2) Open access to and efficient management of song data and voucher specimens, involving the Orthoptera Species File (OSF) and Global Biodiversity Information Facility (GBIF); (3) An infrastructure for automatized analysis and song classification; and (4) Complementation and improvement of Orthoptera Sound Libraries using OSF as the taxonomic backbone and repository for representative song recordings. Taxonomists should be encouraged, or even obliged, to deposit original recordings, particularly if they form part of species descriptions or revisions.

  • Acoustic profiling of Orthoptera for species monitoring and discovery: present state and future needs
    2017
    Co-Authors: Klaus Riede
    Abstract:

    Background: Bioacoustic monitoring and classification of animal communication signals has developed into a powerful tool for measuring and monitoring species diversity within complex communities and habitats. The high number of stridulating species among Orthoptera allows their detection and classification in a non-invasive and economic way, particularly in habitats where visual observations are difficult or even impossible, such as tropical rainforests. Methods: Major Sound archives where queried for Orthoptera songs, with special emphasis on usability as reference training Libraries for computer algorithms. Results: Orthoptera songs are highly stereotyped, reliable taxonomic features. However, exploitation of songs for acoustic profiling is limited by the small number of reference recordings: existing song Libraries represent only about 1,000 species, mainly from Europe and North America, covering less that 10% of extant stridulating Orthoptera species. Available databases are fragmented and lack tools for song annotation and efficient feature-based search. Results from recent bioacoustic surveys illustrate the potential of the method, but also challenges and bottlenecks impeding further progress. A major problem is time-consuming data analysis of recordings. Computer-aided identification software has been developed for classification and identification of cricket and grasshopper songs, but these tools are still far from practical field application. Discussion: A framework for acoustic profiling of Orthoptera should consist of the following components: (1) Protocols for standardised acoustic sampling, at species and community level, using acoustic data loggers for autonomous long-term recordings; (2) Open access to and efficient management of song data and voucher specimens, involving the Orthoptera Species File (OSF) and Global Biodiversity Information Facility (GBIF); (3) An infrastructure for automatised analysis and song classification; (4) Complementation and improvement of Orthoptera Sound Libraries, using Orthoptera Species File as taxonomic backbone and repository for representative song recordings. Taxonomists should be encouraged to deposit original recordings, particularly if they form part of species descriptions or revisions.

Stuart Cunningham - One of the best experts on this subject based on the ideXlab platform.

  • identification of perceptual qualities in textural Sounds using the repertory grid method
    Audio Mostly Conference, 2011
    Co-Authors: Thomas Grill, Arthur Flexer, Stuart Cunningham
    Abstract:

    This paper is about exploring which perceptual qualities are relevant to people listening to textural Sounds. Knowledge about those personal constructs shall eventually lead to more intuitive interfaces for browsing large Sound Libraries. By conducting mixed qualitative-quantitative interviews within the repertory grid framework ten bi-polar qualities are identified. A subsequent web-based study yields measures for inter-rater agreement and mutual similarity of the perceptual qualities based on a selection of 100 textural Sounds. Additionally, some initial experiments are conducted to test standard audio descriptors for their correlation with the perceptual qualities.

  • Audio Mostly Conference - Identification of perceptual qualities in textural Sounds using the repertory grid method
    Proceedings of the 6th Audio Mostly Conference on A Conference on Interaction with Sound - AM '11, 2011
    Co-Authors: Thomas Grill, Arthur Flexer, Stuart Cunningham
    Abstract:

    This paper is about exploring which perceptual qualities are relevant to people listening to textural Sounds. Knowledge about those personal constructs shall eventually lead to more intuitive interfaces for browsing large Sound Libraries. By conducting mixed qualitative-quantitative interviews within the repertory grid framework ten bi-polar qualities are identified. A subsequent web-based study yields measures for inter-rater agreement and mutual similarity of the perceptual qualities based on a selection of 100 textural Sounds. Additionally, some initial experiments are conducted to test standard audio descriptors for their correlation with the perceptual qualities.

Herve Glotin - One of the best experts on this subject based on the ideXlab platform.

  • automatic acoustic detection of birds through deep learning the first bird audio detection challenge
    Methods in Ecology and Evolution, 2019
    Co-Authors: Dan Stowell, Michael D Wood, Hanna Pamula, Yannis Stylianou, Herve Glotin
    Abstract:

    Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their Sounds, and thus passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example Sound Libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects. Multiple methods were able to attain performance of around 88% AUC (area under the ROC curve), much higher performance than previous general‐purpose methods. With modern machine learning including deep learning, general‐purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data with no manual recalibration, and no pre‐training of the detector for the target species or the acoustic conditions in the target environment.

Thomas Grill - One of the best experts on this subject based on the ideXlab platform.

  • identification of perceptual qualities in textural Sounds using the repertory grid method
    Audio Mostly Conference, 2011
    Co-Authors: Thomas Grill, Arthur Flexer, Stuart Cunningham
    Abstract:

    This paper is about exploring which perceptual qualities are relevant to people listening to textural Sounds. Knowledge about those personal constructs shall eventually lead to more intuitive interfaces for browsing large Sound Libraries. By conducting mixed qualitative-quantitative interviews within the repertory grid framework ten bi-polar qualities are identified. A subsequent web-based study yields measures for inter-rater agreement and mutual similarity of the perceptual qualities based on a selection of 100 textural Sounds. Additionally, some initial experiments are conducted to test standard audio descriptors for their correlation with the perceptual qualities.

  • Audio Mostly Conference - Identification of perceptual qualities in textural Sounds using the repertory grid method
    Proceedings of the 6th Audio Mostly Conference on A Conference on Interaction with Sound - AM '11, 2011
    Co-Authors: Thomas Grill, Arthur Flexer, Stuart Cunningham
    Abstract:

    This paper is about exploring which perceptual qualities are relevant to people listening to textural Sounds. Knowledge about those personal constructs shall eventually lead to more intuitive interfaces for browsing large Sound Libraries. By conducting mixed qualitative-quantitative interviews within the repertory grid framework ten bi-polar qualities are identified. A subsequent web-based study yields measures for inter-rater agreement and mutual similarity of the perceptual qualities based on a selection of 100 textural Sounds. Additionally, some initial experiments are conducted to test standard audio descriptors for their correlation with the perceptual qualities.