![enter image description here][1] This is a parallel competition to DCASE2018 challenge task 1A, intended to serve as public leaderboard for participants during the DCASE challenge. The competition is open for everybody and there is no requirement to submit to DCASE challenge. The training material of this competition is the development dataset released for Task1A, and testing material consist of **similar material** to the official evaluation dataset in the DCASE challenge. The testing material amount in this competition is considerably lower than the official evaluation material in the DCASE challenge. There are three parallel competitions to DCASE challenge task 1 open in the Kaggle, one for each subtask: - **[Task1A Leaderboard, Acoustic Scene Classification](https://www.kaggle.com/c/dcase2018-task1a-leaderboard)**, single recording device used in the data collection, no external data allowed. - **[Task1B Leaderboard, Acoustic Scene Classification with mismatched recording devices](https://www.kaggle.com/c/dcase2018-task1b-leaderboard)**, multiple recording devices used in the data collection, no external data allowed. - **[Task1C Leaderboard, Acoustic Scene Classification with use of external data](https://www.kaggle.com/c/dcase2018-task1c-leaderboard)**, the same task setup as in task 1A, but external data is allowed in a limited way. *** ## **Note:** Official DCASE submission is **not done through Kaggle**. See [DCASE website](http://dcase.community/challenge2018/submission) for full information on how to submit to the challenge. *** ### Organizers - [Annamaria Mesaros](http://www.cs.tut.fi/~mesaros/), Postdoctoral researcher, Tampere University of Technology, Finland - [Toni Heittola](http://www.cs.tut.fi/~heittolt/), Researcher, Tampere University of Technology, Finland - [Tuomas Virtanen](http://www.cs.tut.fi/~tuomasv/), Professor, Tampere University of Technology, Finland # Acoustic scene classification task The goal of acoustic scene classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded — for example "park", "pedestrian street", "metro station". ![Overview of acoustic scene classification system.][2] # Audio dataset The dataset for this task is the **TUT Urban Acoustic Scenes 2018** dataset, consisting of recordings from various acoustic scenes. The dataset was recorded in six large European cities, in different locations for each scene class. For each recording location there are 5-6 minutes of audio. The original recordings were split into segments with a length of 10 seconds that are provided in individual files. Available information about the recordings include the following: acoustic scene class, city, and recording location. Acoustic scenes for the task (10): - 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` The dataset was collected by Tampere University of Technology between 01/2018 - 03/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND. [![ERC][3]](https://erc.europa.eu/) **TUT Urban Acoustic Scenes 2018 development dataset** contains only material recorded with device A, having 864 segments for each acoustic scene (144 minutes of audio). The dataset contains in total 8640 segments, i.e. 24 hours of audio. *** ### [TUT Urban Acoustic Scenes 2018, Development dataset (21.1 GB) ](https://zenodo.org/record/1228142) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1228142.svg)](https://doi.org/10.5281/zenodo.1228142) *** **TUT Urban Acoustic Scenes 2018 leaderboard dataset** contains only material recorded with device A, and it is used in this competition as test dataset. This dataset is used only in this competition, not in the actual DCASE2018 Challenge. *** ### [TUT Urban Acoustic Scenes 2018, Leaderboard dataset (2.8 GB) ](https://zenodo.org/record/1245181) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1245181.svg)](https://doi.org/10.5281/zenodo.1245181) *** ## Recording procedure Recordings were made using four devices that captured audio simultaneously. The main recording device consists in [Soundman OKM II Klassik/studio A3](http://www.soundman.de/en/products/), electret binaural microphone and a [Zoom F8](https://www.zoom.co.jp/products/handy-recorder/zoom-f8-multitrack-field-recorder) audio recorder using 48kHz sampling rate and 24 bit resolution. The microphones are specifically made to look like headphones, being worn in the ears. As an effect of this, the recorded audio is very similar to the sound that reaches the human auditory system of the person wearing the equipment. This equipment is further referred to as device A. Three other commonly available customer devices (e.g. smartphones, cameras) were used, handled in typical ways by the person doing recordings (e.g. hand held). We further refer to these devices as B, C, and D. The audio recordings from these devices are of different quality than device A. All simultaneous recordings are time synchronized. ## Baseline system The baseline system provides a simple entry-level state-of-the-art approach that gives reasonable results in the subtasks of Task 1. The baseline system is built on [dcase_util toolbox](https://github.com/DCASE-REPO/dcase_util). *** ### [DCASE2018 Task 1 Baseline code repository](https://github.com/DCASE-REPO/dcase2018_baseline/tree/master/task1) *** Participants are strongly encouraged to build their own systems by extending the provided baseline system. The system has all needed functionality for the dataset handling, acoustic feature storing and accessing, acoustic model training and storing, and evaluation. The modular structure of the system enables participants to modify the system to their needs. The baseline system is a good starting point especially for the entry level researchers to familiarize themselves with the acoustic scene classification problem. If participants plan to publish their code to the DCASE community after the challenge, building their approach on the baseline system will make their code more accessible to the community. DCASE organizers strongly encourage participants to share their code in any form after the challenge. ### System description The baseline system implements a convolutional neural network (CNN) based approach, where log mel-band energies are first extracted for each 10-second signal, and a network consisting of two CNN layers and one fully connected layer is trained to assign scene labels to the audio signals. More detailed description and system accuracy on development dataset can be found from readme file in the [code repository](https://github.com/DCASE-REPO/dcase2018_baseline/blob/master/README.md). [1]: http://d33wubrfki0l68.cloudfront.net/341ee23023e02c446dd7eb9ec3657944829e7dad/5b709/images/logos/dcase/dcase2018_challenge.png [2]: http://d33wubrfki0l68.cloudfront.net/15556a316bea611e8f73980845a7ef41dd162de9/7572a/images/tasks/challenge2018/task1_acoustic_scene_classification.png [3]: http://d33wubrfki0l68.cloudfront.net/750a7452e2c66bfb113a1b011ecc888f03245c99/0e821/images/sponsors/erc.jpg