UNS-Exterior Spatial Sound Events 2023 (UNS-ESSE2023)
Authors/Creators
- 1. Faculty of Technical Sciences, University of Novi Sad, Novi Sad
Description
This dataset was generated within the UNS2: Localising Audio Events in Crowds use case of the H2020 MARVEL project. The purpose of the dataset is to offer audio samples collected outdoors in an urban area that could be used for development of sound event localisation and detection (SELD) models for acoustic monitoring in urban environments.
The dataset was generated within a staged recording process. Audio samples were collected by utilising Infineon Audiohub Nano 8-channel microphone array board with a sampling rate of 48 kHz. The audio was synthetised by mixing target sound events from the FSD50K dataset “gunshot” and “gunfire”, “boom”, and “shatter” with samples of the class “chatter”, that was used as a background noise, extracted also from the FSD50k dataset. The scenario included different SNR values for the events in the mixtures and the measurement of the Sound Pressure Level (SPL) before and after the recording. Sound events were reproduced using eight JBL VP7212MDP10 speakers, which were positioned circularly around the microphone array board in equidistant positions. Data collection was performed for two different distances: 5 m and 10 m. One of the speakers was used to reproduce the target events (overlaid on the background noise), where the selected speaker varied during the data collection, while the others were used to reproduce background noise only. The recording setup was placed outdoors, involving additional ambient noise of the urban city area, which is a case closer to the real-world scenario comparing to the datasets recorded in the laboratory conditions. Together with the audio files, spatiotemporal annotation of the sound events is provided. These annotations include temporal onset and offset of the target events, azimuth, source distance, SNR level and SPL values for background ambience noise.
This work was funded by the European Union’s Horizon 2020 research and innovation program MARVEL under grant agreement No 957337. This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.
Notes (English)
Files
annotations.zip
Files
(29.2 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:7c8e061cfed80ca595b9d7e07d934e5c
|
97.5 kB | Download |
|
md5:654b52393b10f8d3c4191b9e974437f2
|
262.8 kB | Preview Download |
|
md5:d19b482efe763e11a18405b1b9c5f54e
|
29.2 GB | Preview Download |
|
md5:c69431c45e9717a791ac5659f933f775
|
283.2 kB | Preview Download |
|
md5:b29fe427295b6cbaa458875730dd392c
|
151.3 kB | Preview Download |
|
md5:69218e83f9057af55237c0b79acaa088
|
6.9 kB | Preview Download |