Bandara, Meelan, Jayasundara, Roshinie, Ariyarathne, Isuru, Meedeniya, Dulani and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2023. Forest Sound Classification Dataset: FSC22. Sensors 23 (4) , 2032. 10.3390/s23042032 |
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (6MB) | Preview |
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.
Item Type: | Article |
---|---|
Date Type: | Published Online |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA76 Computer software |
Publisher: | MDPI |
ISSN: | 1424-8220 |
Date of First Compliant Deposit: | 8 February 2023 |
Date of Acceptance: | 7 February 2023 |
Last Modified: | 05 May 2023 21:25 |
URI: | https://orca.cardiff.ac.uk/id/eprint/156541 |
Citation Data
Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
Edit Item |