path
stringlengths 20
23
| classname
stringclasses 15
values |
---|---|
audios/a107_30_40.wav | cafe/restaurant |
audios/b020_200_210.wav | beach |
audios/b020_20_30.wav | beach |
audios/b097_120_130.wav | car |
audios/a090_130_140.wav | train |
audios/a154_270_280.wav | tram |
audios/a020_40_50.wav | park |
audios/a025_80_90.wav | beach |
audios/b037_160_170.wav | library |
audios/b113_220_230.wav | office |
audios/a126_220_230.wav | cafe/restaurant |
audios/b053_50_60.wav | office |
audios/a059_20_30.wav | forest_path |
audios/b047_110_120.wav | library |
audios/b048_60_70.wav | library |
audios/a124_290_300.wav | city_center |
audios/b091_122_132.wav | city_center |
audios/b102_10_20.wav | grocery_store |
audios/b112_173_183.wav | bus |
audios/a014_30_40.wav | beach |
audios/a113_30_40.wav | grocery_store |
audios/a128_41_51.wav | city_center |
audios/b030_240_250.wav | home |
audios/a038_40_50.wav | home |
audios/a140_210_220.wav | bus |
audios/a130_10_20.wav | cafe/restaurant |
audios/b113_50_60.wav | office |
audios/b094_90_100.wav | city_center |
audios/b066_20_30.wav | train |
audios/a083_0_10.wav | cafe/restaurant |
audios/a152_220_230.wav | tram |
audios/b089_210_220.wav | car |
audios/a096_0_10.wav | forest_path |
audios/a066_100_110.wav | forest_path |
audios/b008_110_120.wav | residential_area |
audios/a112_60_70.wav | bus |
audios/a104_90_100.wav | bus |
audios/b081_120_130.wav | tram |
audios/a026_120_130.wav | beach |
audios/a005_0_10.wav | city_center |
audios/a011_10_20.wav | residential_area |
audios/a025_20_30.wav | beach |
audios/a001_50_60.wav | residential_area |
audios/b020_160_170.wav | beach |
audios/b035_0_10.wav | library |
audios/a151_230_240.wav | tram |
audios/a112_100_110.wav | bus |
audios/a155_260_270.wav | metro_station |
audios/a124_250_260.wav | city_center |
audios/b015_40_50.wav | park |
audios/b064_70_80.wav | train |
audios/b007_140_150.wav | residential_area |
audios/b096_240_250.wav | car |
audios/a089_120_130.wav | metro_station |
audios/a057_30_40.wav | forest_path |
audios/a105_90_100.wav | grocery_store |
audios/b060_100_110.wav | train |
audios/a071_170_180.wav | bus |
audios/b054_60_70.wav | office |
audios/a034_70_80.wav | home |
audios/a142_98_108.wav | bus |
audios/b069_250_260.wav | metro_station |
audios/b008_0_10.wav | residential_area |
audios/b002_170_180.wav | residential_area |
audios/a082_160_170.wav | cafe/restaurant |
audios/b010_130_140.wav | beach |
audios/a015_50_60.wav | beach |
audios/a059_120_130.wav | forest_path |
audios/a001_160_170.wav | residential_area |
audios/b078_120_130.wav | tram |
audios/b029_220_230.wav | home |
audios/a023_90_100.wav | beach |
audios/b035_90_100.wav | library |
audios/b095_70_80.wav | city_center |
audios/b025_90_100.wav | library |
audios/a110_100_110.wav | grocery_store |
audios/b091_182_192.wav | city_center |
audios/b084_200_210.wav | grocery_store |
audios/a029_70_80.wav | home |
audios/b008_70_80.wav | residential_area |
audios/b096_280_290.wav | car |
audios/b092_20_30.wav | city_center |
audios/a155_200_210.wav | metro_station |
audios/a138_40_50.wav | car |
audios/a094_60_70.wav | car |
audios/b032_20_30.wav | home |
audios/a127_260_270.wav | city_center |
audios/a008_130_140.wav | residential_area |
audios/a147_210_220.wav | tram |
audios/b029_40_50.wav | home |
audios/a092_190_200.wav | train |
audios/a097_100_110.wav | forest_path |
audios/a108_190_200.wav | cafe/restaurant |
audios/a100_190_200.wav | office |
audios/b031_200_210.wav | home |
audios/a089_270_280.wav | metro_station |
audios/a082_150_160.wav | cafe/restaurant |
audios/b010_0_10.wav | beach |
audios/a084_202_212.wav | metro_station |
audios/b093_50_60.wav | city_center |
End of preview. Expand
in Dataset Viewer.
TUT2017
This is an audio classification dataset for Acoustic Scene Classification.
Classes = 15 , Split = four-fold
Structure
audios
folder contains audio files.csv_files
folder contains CSV files for four-fold cross-validation.- To perform cross-validation on fold 1,
train_1.csv
will be used for the training split andtest_1.csv
for the testing split, with the same pattern followed for the other folds. - To perform training and testing witout cross-validation, use
csv_files/train.csv
andcsv_files/test.csv
files respectively.
Download
import os
import huggingface_hub
audio_datasets_path = "DATASET_PATH/Audio-Datasets"
if not os.path.exists(audio_datasets_path): print(f"Given {audio_datasets_path=} does not exist. Specify a valid path ending with 'Audio-Datasets' folder.")
huggingface_hub.snapshot_download(repo_id="MahiA/TUT2017", repo_type="dataset", local_dir=os.path.join(audio_datasets_path, "TUT2017"))
Acknowledgment
This dataset is a slightly processed/restructured version of data originally released by Source.
Please refer to the respective source for their licensing details and any additional information.
Contact
For questions or feedback, please create an issue.
- Downloads last month
- 239