Datasets › TUT Acoustic Scenes 2017
TUT Acoustic Scenes 2017
The TUT Acoustic Scenes 2017 dataset is a collection of recordings from various acoustic scenes all from distinct locations. For each recording location 3-5 minute long audio recordings are captured and are split into 10 seconds which act as unit of sample for this task. All the audio clips are recorded with 44.1 kHz sampling rate and 24 bit resolution.
Source: Ensemble of deep neural networks for acoustic scene classification Image Source: https://www.mathworks.com/help/audio/ug/acoustic-scene-recognition-using-late-fusion.html;jsessionid=95c969bc690c06fe42a7ed17f57e
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Acoustic Scene Classification | TUT Acoustic Scenes 2017 | Qwen-Audio 1:1 Accuracy 0.649 | Qwen-Audio: Advancing Universal Audio Understanding via... | alibaba-damo-academy/FunASR +1 | 1 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 13. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models | 2 | 1 | 14 Nov 2023 | ran 5 of 7 samples (2 unverified; 7 pointer-only for licence) |
Dataset loaders archive 2025-07-28
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Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
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Variants archive 2025-07-28
- TUT Acoustic Scenes 2017
1 variant name, as the archive lists them.
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