{"url":"/dataset/tau-urban-acoustic-scenes-2019","name":"TAU Urban Acoustic Scenes 2019","full_name":"TAU Urban Acoustic Scenes 2019","description_markdown":"**TAU Urban Acoustic Scenes 2019** development dataset consists of 10-seconds audio segments from 10 acoustic scenes: airport, indoor shopping mall, metro station, pedestrian street, public square, street with medium level of traffic, travelling by a tram, travelling by a bus, travelling by an underground metro and urban park. Each acoustic scene has 1440 segments (240 minutes of audio). The dataset contains in total 40 hours of audio.\n\nSource: [https://zenodo.org/record/2589280](https://zenodo.org/record/2589280)\nImage Source: [http://dcase.community/challenge2019/task-acoustic-scene-classification#citation](http://dcase.community/challenge2019/task-acoustic-scene-classification#citation)","description_withheld":null,"homepage":"https://zenodo.org/record/2589280","introduced_date":"2018-01-01","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/2589280"},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Keyword Spotting","url":"/task/keyword-spotting","datasets_with_task":"/datasets/task/keyword-spotting"},{"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":["TAU Urban Acoustic Scenes 2019"],"data_loaders":[],"num_papers_in_archive":14,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/keyword-spotting-on-tau-urban-acoustic-scenes","task":"Keyword Spotting","dataset_variant":"TAU Urban Acoustic Scenes 2019","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"CP-ResNet(ch64) w/ SSN(S=2, A=Sub)","paper":"/paper/subspectral-normalization-for-neural-audio","metrics":{"Accuracy":"83.6% ±0.07"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/acoustic-scene-classification-on-tau-urban","task":"Acoustic Scene Classification","dataset_variant":"TAU Urban Acoustic Scenes 2019","rows":1,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"Two-stage ensemble system","paper":"/paper/a-two-stage-approach-to-device-robust","metrics":{"1:1 Accuracy":"81.9"},"code_links":[{"title":"MihawkHu/DCASE2020_task1","url":"https://github.com/MihawkHu/DCASE2020_task1"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/subspectral-normalization-for-neural-audio","title":"SubSpectral Normalization for Neural Audio Data Processing","date":"2021-03-25","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/a-two-stage-approach-to-device-robust","title":"A Two-Stage Approach to Device-Robust Acoustic Scene Classification","date":"2020-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}