M. Sankupellay, Tshering Dema, S. Tarar, M. Towsey, A. Truskinger, M. Brereton, P. Roe
{"title":"Visual Analytics of Eco-Acoustic Recordings: The Use of Acoustic Indices to Visualise 24-Hour Recordings","authors":"M. Sankupellay, Tshering Dema, S. Tarar, M. Towsey, A. Truskinger, M. Brereton, P. Roe","doi":"10.1109/BDVA.2016.7787051","DOIUrl":null,"url":null,"abstract":"Audio recording is a convenient and important method for large-scale terrestrial environmental monitoring. However, it is impossible to listen and make sense of all the data collected. Attempts to generalise automated analysis tasks have not been successful due to the unconstrained nature of long-term environmental recording. Our approach to this big-data challenge is to facilitate visualisation of long-term audio recording, to keep ecologists in the loop. The content of long-duration audio recordings are visualised by calculating acoustic indices. Our interface facilitates the customised visualisation and navigation of long-term audio recording by ecologists. Two case studies, one in Australia and one in Bhutan, are presented as examples.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Big Data Visual Analytics (BDVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BDVA.2016.7787051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Audio recording is a convenient and important method for large-scale terrestrial environmental monitoring. However, it is impossible to listen and make sense of all the data collected. Attempts to generalise automated analysis tasks have not been successful due to the unconstrained nature of long-term environmental recording. Our approach to this big-data challenge is to facilitate visualisation of long-term audio recording, to keep ecologists in the loop. The content of long-duration audio recordings are visualised by calculating acoustic indices. Our interface facilitates the customised visualisation and navigation of long-term audio recording by ecologists. Two case studies, one in Australia and one in Bhutan, are presented as examples.