dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (UNESP)
dc.contributorUniversity College Cork
dc.date.accessioned2022-05-01T06:02:14Z
dc.date.accessioned2022-12-20T03:38:14Z
dc.date.available2022-05-01T06:02:14Z
dc.date.available2022-12-20T03:38:14Z
dc.date.created2022-05-01T06:02:14Z
dc.date.issued2021-07-01
dc.identifierInformation (Switzerland), v. 12, n. 7, 2021.
dc.identifier2078-2489
dc.identifierhttp://hdl.handle.net/11449/233242
dc.identifier10.3390/info12070265
dc.identifier2-s2.0-85109387867
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5413341
dc.description.abstractLabeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at 94.58% in the prediction of labeling for birds and insects; and 91.09% for the prediction of the sound event as frogs and insects.
dc.languageeng
dc.relationInformation (Switzerland)
dc.sourceScopus
dc.subjectActive learning
dc.subjectClustering
dc.subjectLabeling
dc.subjectSampling
dc.subjectSoundscape ecology
dc.subjectVisualization
dc.titleVisual active learning for labeling: A case for soundscape ecology data
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución