{"url":"/dataset/dcase-2019-mobile","name":"DCASE 2019 Mobile","full_name":"TAU Urban Acoustic Scenes 2019 Mobile","description_markdown":"**TAU Urban Acoustic Scenes 2019 Mobile** development dataset consists of 10-seconds audio segments from 10 acoustic scenes:\r\n\r\n    Airport\r\n    Indoor shopping mall\r\n    Metro station\r\n    Pedestrian street\r\n    Public square\r\n    Street with medium level of traffic\r\n    Travelling by a tram\r\n    Travelling by a bus\r\n    Travelling by an underground metro\r\n    Urban park\r\n\r\nRecordings were made with three devices that captured audio simultaneously. Each acoustic scene has 1440 segments (240 minutes of audio) recorded with device A (main device) and 108 segments of parallel audio (18 minutes) each recorded with devices B and C. The dataset contains in total 46 hours of audio.\r\n\r\n[DCASE website](http://dcase.community/challenge2019/task-acoustic-scene-classification#download)\r\n\r\nSource: [Zenodo](https://zenodo.org/record/2589332) \r\nImage Source: [Acoustic Scene Classification](http://dcase.community/challenge2019/task-acoustic-scene-classification)","description_withheld":null,"homepage":"https://zenodo.org/record/2589332","introduced_date":"2018-07-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-multi-device-dataset-for-urban-acoustic","title":"A multi-device dataset for urban acoustic scene classification","first_author":"Annamaria Mesaros","url":null},"license":{"name":"Other (Non-Commercial)","url":"https://zenodo.org/record/2589332"},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Acoustic Scene Classification","url":"/task/acoustic-scene-classification","datasets_with_task":"/datasets/task/acoustic-scene-classification"},{"name":"Scene Classification","url":"/task/scene-classification","datasets_with_task":"/datasets/task/scene-classification"}],"languages":[],"variants":["DCASE 2019 Mobile"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/acoustic-scene-classification-on-dcase-2019","task":"Acoustic Scene Classification","dataset_variant":"DCASE 2019 Mobile","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Basic + Spectrum Correction","paper":"/paper/spectrum-correction-acoustic-scene","metrics":{"Accuracy":"70.4"},"code_links":[{"title":"SRPOL-AUI/spectrum-correction","url":"https://github.com/SRPOL-AUI/spectrum-correction"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/spectrum-correction-acoustic-scene","title":"Spectrum Correction: Acoustic Scene Classification with Mismatched Recording Devices","date":"2021-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}