{"url":"/dataset/audio-de-mosquitos-aedes-aegypti","name":"Audio de mosquitos Aedes Aegypti","full_name":"Wing beats","description_markdown":"**Dataset Description:**\r\n\r\nThe dataset comprises audio recordings of the wing beats of Aedes aegypti mosquitoes and others, conducted in a semi-controlled environment. It encompasses approximately 18,706 seconds of recording, with properly labeled samples.\r\n\r\n**Dataset Characteristics:**\r\n\r\n- **Data Type:** Audio recordings.\r\n- **Total Duration:** Approximately 18,706 seconds.\r\n- **Labeling:** Labeled samples.\r\n- **Environment:** Semi-controlled.\r\n- **Data Type:** Audio recordings.\r\n- **Total Duration:** Approximately 18,706 seconds.\r\n- **Species:** Aedes aegypti and others.","description_withheld":null,"homepage":"https://www.inf.ufrgs.br/aedes-vigilance/","introduced_date":"2023-06-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/acoustic-identification-of-ae-aegypti","title":"Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks","first_author":"Kayuã Oleques Paim","url":null},"license":{"name":"License Attribution 4.0 International (CC BY 4.0)","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Sound Classification","url":"/task/sound-classification","datasets_with_task":"/datasets/task/sound-classification"}],"languages":[],"variants":["Audio de mosquitos Aedes Aegypti"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}