TUT Sound events 2016, Development dataset

Audio Research Group / Tampere University of Technology

Authors
- Toni Heittola (toni.heittola@tut.fi, http://www.cs.tut.fi/~heittolt/)
- Annamaria Mesaros (annamaria.mesaros@tut.fi, http://www.cs.tut.fi/~mesaros/)
- Tuomas Virtanen (tuomas.virtanen@tut.fi, http://www.cs.tut.fi/~tuomasv/)

Recording and annotation
- Eemi Fagerlund
- Aku Hiltunen

Table of Contents

  1. Dataset
  2. Usage
  3. Changelog
  4. License

1. Dataset

TUT Sound events 2016, development dataset consists of 22 audio recordings from two acoustic scenes:

The dataset was collected in Finland by Tampere University of Technology between 06/2015 - 01/2016. The data collection has received funding from the European Research Council under the ERC Grant Agreement 637422 EVERYSOUND.

ERC

Preparation of the dataset

The recordings were captured each in a different location: different streets, different homes. The equipment used for recording consists of a binaural Soundman OKM II Klassik/studio A3 electret in-ear microphone and a Roland Edirol R-09 wave recorder using 44.1 kHz sampling rate and 24 bit resolution.

For audio material recorded in private places, written consent was obtained from all people involved. Material recorded in public places (residential area) does not require such consent.

Individual sound events in each recording were annotated by two research assistants using freely chosen labels for sounds. Annotators were trained first on few example recordings. They were instructed to annotate all audible sound events, and choose event labels freely. This resulted in a large set of raw labels. Target sound event classes for the dataset were selected based on the frequency of the obtained labels, to ensure that the selected sounds are common for an acoustic scene, and there are sufficient examples for learning acoustic models.

The ground truth is provided as a list of the sound events present in the recording, with annotated onset and offset for each sound instance. Annotations with only targeted sound events classes are in the directory meta, and annotations with full set of annotated sound events are in the directory meta_full. Only targeted sound events should be evaluated.

File structure

dataset root
│   README.md               this file, markdown-format
│   README.html             this file, html-format
│   EULA.pdf                End user license agreement
│   meta.txt                meta data, csv-format, [audio file][tab][scene label][tab][event onset][tab][event offset][tab][event label][tab][event type]
│
└───audio                   22 audio files, 24-bit 44.1kHz
│   │
│   └───home                acoustic scene label
│   │   │   a030.wav        name format: [original_recording_identifier].wav
│   │   │   a031.wav
│   │       ...
│   └───residential_area    
│       │   a001.wav        
│       │   a002.wav
│           ...
│
└───evaluation_setup        cross-validation setup, 4 folds
│   │   home_fold1_train.txt        training file list, csv-format: [audio file (string)][tab][scene label (string)][tab][event onset (float)][tab][event offset (float)][tab][event label (string)]
│   │   home_fold1_test.txt         testing file list, csv-format: [audio file (string)][tab][scene label (string)]
│   │   home_fold1_evaluate.txt     evaluation file list, fold1_test.txt with added ground truth, csv-format: [audio file (string)][tab][scene label (string)][tab][event onset (float)][tab][event offset (float)][tab][event label (string)]
│   │   ...        
│
└───meta                    meta data, only with target sound classes
│   │
│   └───home                acoustic scene label
│   │   │   a030.ann        annotation data, csv-format (can be imported to audacity): [event onset (float)][tab][event offset (float)][tab][event label (string)]
│   │   │   a031.ann
│   │       ...
│   └───residential_area    
│       │   a001.ann        
│       │   a002.ann
│           ...
└───meta_full               meta data, all with all sound classes
    │
    └───home                acoustic scene label
    │   │   a030_full.ann   full annotation data, csv-format (can be imported to audacity): [event onset (float)][tab][event offset (float)][tab][event label (string)]
    │   │   a031_full.ann
    │       ...
    └───residential_area    
        │   a001_full.ann       
        │   a002_full.ann    
            ...

Event statistics

The sound event instance counts for the dataset are shown below. It can be observed that in residential area scenes, the sound event classes are mostly related to concrete physical sound sources - bird singing, car passing by - while the home scenes are dominated by abstract object impact sounds, besides some more well defined (still impact) dishes, cutlery, etc.

Home

Event label Event count
(object) rustling 41
(object) snapping 42
cupboard 27
cutlery 56
dishes 94
drawer 23
glass jingling 26
object impact 155
people walking 24
washing dishes 60
water tap running 37
Total 585

Residential area

Event label Event count
(object) banging 15
bird singing 162
car passing by 74
children shouting 23
people speaking 41
people walking 32
wind blowing 22
Total 369

2. Usage

Partitioning of data into development dataset and evaluation dataset was done based on the amount of examples available for each event class, while also taking into account recording location. Ideally the subsets should have the same amount of data for each class, or at least the same relative amount, such as a 70-30% split. Because the event instances belonging to different classes are distributed unevenly within the recordings, the partitioning of individual classes can be controlled only to a certain extent.

The split condition was relaxed from 70-30%. For home, 40-80% of instances of each class were selected into the development set. For residential area, 60-80% of instances of each class were selected into the development set.

Evaluation dataset is provided separately.

Cross-validation setup

A cross-validation setup is provided in order to make results reported with this dataset uniform. The setup consists of four folds, so that each recording is used exactly once as test data. At this stage the only condition imposed was that the test subset does not contain classes unavailable in training.

The folds are provided with the dataset in the directory evaluation_setup.

Training

evaluation setup\[scene_label]_fold[1-4]_train.txt
training file list (in csv-format)

Format:

[audio file (string)][tab][scene label (string)][tab][event onset (float)][tab][event offset (float)][tab][event label (string)]

Testing

evaluation setup\[scene_label]_fold[1-4]_test.txt
testing file list (in csv-format)

Format:

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

Evaluating

evaluation setup\[scene_label]_fold[1-4]_evaluate.txt
evaluation file list (in csv-format), same as fold[1-4]_test.txt but added with ground truth information. These two files are provided separately to prevent contamination with ground truth when testing the system.

Format: 

[audio file (string)][tab][scene label][tab][event onset (float)][tab][event offset (float)][tab][event label (string)]

3. Changelog

1.0 / 2016-02-05

4. License

See file EULA.pdf