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LICENSE

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+Copyright (c) 2020 Tampere University and its licensors
+All rights reserved.
+Permission is hereby granted, without written agreement and without license or royalty
+fees, to use and copy the TAU Urban Acoustic Scenes 2020 Mobile (“Work”) described in this document
+and composed of audio and metadata. This grant is only for experimental and non-commercial
+purposes, provided that the copyright notice in its entirety appear in all copies of this Work,
+and the original source of this Work, (Audio Research Group at Tampere University),
+is acknowledged in any publication that reports research using this Work.
+Any commercial use of the Work or any part thereof is strictly prohibited.
+Commercial use include, but is not limited to:
+- selling or reproducing the Work
+- selling or distributing the results or content achieved by use of the Work
+- providing services by using the Work.
+
+IN NO EVENT SHALL TAMPERE UNIVERSITY OR ITS LICENSORS BE LIABLE TO ANY PARTY
+FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE
+OF THIS WORK AND ITS DOCUMENTATION, EVEN IF TAMPERE UNIVERSITY OR ITS
+LICENSORS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+TAMPERE UNIVERSITY AND ALL ITS LICENSORS SPECIFICALLY DISCLAIMS ANY
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS FOR A PARTICULAR PURPOSE. THE WORK PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND
+THE TAMPERE UNIVERSITY HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT,
+UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

README.md

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+---
+license: other
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: name
+    dtype: string
+configs:
+- config_name: default
+  data_files:
+  - split: dev
+    path: "data/dev-*.parquet"
+  - split: eval
+    path: "data/eval-*.parquet"
+---
+
+# TUT Urban Acoustic Scenes 2020 Mobile Development and Evaluation datasets
+
+This is a mirror of the TUT Urban Acoustic Scenes 2020 Mobile [Development](https://zenodo.org/records/3819968) and [Evaluation](https://zenodo.org/records/3685828) datasets.
+The original files were converted from WAV to Opus to reduce the size and accelerate streaming.
+
+- **Sampling rate**: 48 kHz (resampled from 44.1 kHz to support Opus format)
+- **Channels**: 1
+- **Format**: Opus
+- **Splits**:
+  - **Dev**: 64 hours, 23035 clips.
+  - **Eval**: 33 hours, 11880 clips.
+- **License**: Custom. See `LICENSE` file.
+- **Source**: https://zenodo.org/records/3819968 and https://zenodo.org/records/3685828
+- **Paper**: [TUT Database for Acoustic Scene Classification and Sound Event Detection](https://doi.org/10.1109/EUSIPCO.2016.7760424)
+
+See [here](https://dcase.community/challenge2020/task-acoustic-scene-classification) for more details about the dataset.
+
+## Usage
+
+```python
+import io
+
+import soundfile as sf
+from datasets import Features, Value, load_dataset
+
+for item in load_dataset(
+    "philgzl/tut2020",
+    split="dev",
+    streaming=True,
+    features=Features({"audio": Value("binary"), "name": Value("string")}),
+):
+    print(item["name"])
+    buffer = io.BytesIO(item["audio"])
+    x, fs = sf.read(buffer)
+    # do stuff...
+```
+
+## Citation
+
+```bibtex
+@inproceedings{mesaros2016tut,
+  title = {{TUT} database for acoustic scene classification and sound event detection},
+  author = {Mesaros, Annamaria and Heittola, Toni and Virtanen, Tuomas},
+  booktitle = {Proc. EUSIPCO},
+  pages = {1128--1132},
+  year = {2016},
+}
+```

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