TUT Urban Acoustic Scenes 2018, Development dataset

Audio Research Group / Tampere University of Technology

Authors

Recording and annotation

  • Ronal Bejarano Rodriguez
  • Eemi Fagerlund
  • Aino Koskimies
  • Toni Heittola

1. Dataset

TUT Urban Acoustic Scenes 2018 development dataset consists of 10-seconds audio segments from 10 acoustic scenes:

  • Airport - airport
  • Indoor shopping mall - shopping_mall
  • Metro station - metro_station
  • Pedestrian street - street_pedestrian
  • Public square - public_square
  • Street with medium level of traffic - street_traffic
  • Travelling by a tram - tram
  • Travelling by a bus - bus
  • Travelling by an underground metro - metro
  • Urban park - park

Each acoustic scene has 864 segments (144 minutes of audio). The dataset contains in total 24 hours of audio.

The dataset was collected in Finland by Tampere University of Technology between 02/2018 - 03/2018. The data collection has received funding from the European Research Council under the ERC Grant Agreement 637422 EVERYSOUND.

ERC

Preparation of the dataset

The dataset was recorded in six large European cities: Barcelona, Helsinki, London, Paris, Stockholm, and Vienna. For all acoustic scenes, audio was captured in multiple locations: different streets, different parks, different shopping malls. In each location, multiple 2-3 minute long audio recordings were captured in a few slightly different positions (2-4) within the selected location. Collected audio material was cut into segments of 10 seconds length.

The equipment used for recording consists of a binaural Soundman OKM II Klassik/studio A3 electret in-ear microphone and a Zoom F8 audio recorder using 48 kHz sampling rate and 24 bit resolution. During the recording, the microphones were worn by the recording person in the ears, and head movement was kept to minimum.

Post-processing of the recorded audio involves aspects related to privacy of recorded individuals, and possible errors in the recording process. The material was screened for content, and segments containing close microphone conversation were eliminated. Some interferences from mobile phones are audible, but are considered part of real-world recording process.

Dataset statistics

The dataset is perfectly balanced at acoustic scene level, with very slight differences in the number of segments from each city.

Audio segments

Scene class Segments Barcelona Helsinki London Paris Stockholm Vienna
Airport 864 128 149 145 156 158 128
Bus 864 144 144 144 144 144 144
Metro 864 141 144 146 144 145 144
Metro station 864 144 144 144 144 144 144
Park 864 144 144 144 144 144 144
Public square 864 144 144 144 144 144 144
Shopping mall 864 144 144 144 144 144 144
Street, pedestrian 864 145 145 145 144 145 140
Street, traffic 864 144 144 144 144 144 144
Tram 864 143 145 144 144 144 144
Total 8640 1421 1447 1444 1452 1456 1420

Recording locations

Scene class Location Barcelona Helsinki London Paris Stockholm Vienna
Airport 22 4 3 3 4 5 3
Bus 36 4 4 7 11 6 4
Metro 29 3 5 4 9 4 4
Metro station 40 5 6 12 9 4 4
Park 25 4 4 4 4 5 4
Public_square 24 4 4 4 4 4 4
Shopping mall 22 4 4 2 4 4 4
Street, pedestrian 28 7 4 4 5 4 4
Street, traffic 25 4 4 5 4 4 4
Tram 35 4 4 9 9 5 4
Total 286 43 42 54 63 45 39

File structure

dataset root
│   README.md               this file, markdown-format
│   README.html             this file, html-format
│   meta.csv                meta data, csv-format with a header row, [audio file (string)][tab][scene label (string)][tab][identifier (string)][tab][source_label (string)]
│
└───audio                   8640 audio segments, 24-bit 48kHz stereo
│   │   airport-barcelona-0-0-a.wav     file naming convention: [scene label]-[city]-[location id]-[segment id]-[device id].wav
│   │   airport-barcelona-0-1-a.wav
│   │   airport-barcelona-0-3-a.wav
│   │   ...
│   │   airport-barcelona-1-17-a.wav
│   │   airport-barcelona-1-18-a.wav
│   │   ...
│
└───evaluation_setup        cross-validation setup, 1 fold
    │   fold1_train.txt     training file list, csv-format, [audio file (string)][tab][scene label (string)]
    │   fold1_test.txt      testing file list, csv-format, [audio file (string)]
    │   fold1_evaluate.txt  evaluation file list, fold1_test.txt with added ground truth, csv-format, [audio file (string)][tab][scene label (string)]

2. Usage

The partitioning of the data was done based on the location of the original recordings. All segments recorded at the same location were included into a single subset - either development dataset or evaluation dataset. For each acoustic scene, 864 segments were included in the development dataset provided here. Evaluation dataset is provided separately.

Training / test setup

A suggested training/test partitioning of the development set is provided in order to make results reported with this dataset uniform. The partitioning is done such that the segments recorded at the same location are included into the same subset - either training or testing. The partitioning is done aiming for a 70/30 ratio between the number of segments in training and test subsets while taking into account recording locations, and selecting the closest available option.

The setup is provided with the dataset in the directory evaluation_setup.

Statistics

Scene class Train / Segments Train / Locations Test / Segments Test / Locations
Airport 599 15 265 7
Bus 622 26 242 10
Metro 603 20 261 9
Metro station 605 28 259 12
Park 622 18 242 7
Public square 648 18 216 6
Shopping mall 585 16 279 6
Street, pedestrian 617 20 247 8
Street, traffic 618 18 246 7
Tram 603 24 261 11
Total 6122 203 2518 83

Training

evaluation setup\fold1_train.txt
training file list (in csv-format)

Format:

[audio file (string)][tab][scene label (string)]

Testing

evaluation setup\fold1_test.txt
testing file list (in csv-format)

Format: [audio file (string)]

Evaluating

evaluation setup\fold1_evaluate.txt
evaluation file list (in csv-format), same as fold1_test.txt but with additional reference information. These two files are provided separately to prevent contamination with ground truth when testing the system

Format: 

[audio file (string)][tab][scene label (string)]

Custom setups

If not using the provided training/test setup, pay attention to the segments recorded at the same location. Location identifier can be found from meta.csv or from audio file names:

[scene label]-[city]-[location id]-[segment id]-[device id].wav

Make sure that all files having same location id are placed on the same side of the evaluation. In this dataset, device id is always the same (a).

3. Changelog

v1.0 / 2018-04-24

  • Initial commit

4. License

Copyright (c) 2018 Tampere University of Technology and its licensors 
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and the original source of this Work, (Audio Research Group from Laboratory of Signal
Processing at Tampere University of Technology), 
is acknowledged in any publication that reports research using this Work.
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