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dc.contributor.author Bandara, M
dc.contributor.author Jayasundara, R
dc.contributor.author Ariyarathne, I
dc.contributor.author Meedeniya, D
dc.contributor.author Perera, C
dc.date.accessioned 2023-12-01T08:41:18Z
dc.date.available 2023-12-01T08:41:18Z
dc.date.issued 2023
dc.identifier.citation Bandara, M., Jayasundara, R., Ariyarathne, I., Meedeniya, D., & Perera, C. (2023). Forest Sound Classification Dataset: FSC22. Sensors, 23(4), Article 4. https://doi.org/10.3390/s23042032 en_US
dc.identifier.issn 1424-8220 en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21880
dc.description.abstract The study of environmental sound classification (ESC) has become popular over the years due to the intricate nature of environmental sounds and the evolution of deep learning (DL) techniques. Forest ESC is one use case of ESC, which has been widely experimented with recently to identify illegal activities inside a forest. However, at present, there is a limitation of public datasets specific to all the possible sounds in a forest environment. Most of the existing experiments have been done using generic environment sound datasets such as ESC-50, U8K, and FSD50K. Importantly, in DL-based sound classification, the lack of quality data can cause misguided information, and the predictions obtained remain questionable. Hence, there is a requirement for a well-defined benchmark forest environment sound dataset. This paper proposes FSC22, which fills the gap of a benchmark dataset for forest environmental sound classification. It includes 2025 sound clips under 27 acoustic classes, which contain possible sounds in a forest environment. We discuss the procedure of dataset preparation and validate it through different baseline sound classification models. Additionally, it provides an analysis of the new dataset compared to other available datasets. Therefore, this dataset can be used by researchers and developers who are working on forest observatory tasks. en_US
dc.language.iso en en_US
dc.publisher Multidisciplinary Digital Publishing Institute en_US
dc.subject forest acoustic dataset en_US
dc.subject environment sound classification en_US
dc.subject machine learning en_US
dc.subject Freesound en_US
dc.subject deep learning en_US
dc.title Forest sound classification dataset: FSC22 en_US
dc.type Article-Full-text en_US
dc.identifier.year 2023 en_US
dc.identifier.journal Sensors en_US
dc.identifier.issue 4 en_US
dc.identifier.volume 23 en_US
dc.identifier.database MDPI en_US
dc.identifier.pgnos 2032 en_US
dc.identifier.doi https://doi.org/10.3390/s23042032 en_US


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