Group in Noise (GiN) data

DOI: 10.14469/hpc/13463 Metadata

Created: 2023-12-01 23:04

Last modified: 2024-01-09 09:07

Author: Emilie d'Olne

License: Creative Commons: Attribution 4.0

Funding: (none given)

Description

This collection contains the Group in Noise (GiN) data presented in (d'Olne et al., 2023). The data contain clock-synchronised audio and head pose recordings of real conversations in restaurant noise. The recordings took place in 3 rooms of the Electrical and Electronic Engineering department with 6 seated participants and a 7th ambulant 'waiter'. One participant was equipped with a glasses-mounted microphone array and binaural microphones, providing ego-centric data of the scene. The dataset contains approximately 2 hours of audio. Useful tools and automatic download instructions can be found in https://github.com/ImperialCollegeLondon/sap-ic-gin.

Files

FilenameSizeTypeDescription
GiNdata.zip 575MB application/zip .zip archive containing the GiN dataset
GiNdata.mnpub 0 chemical/x-mnpub Mestrenova signature file for GiNdata.zip
GiNdata.z01 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z02 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z03 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z04 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z05 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z06 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z07 943MB application/octet-stream .zip archive containing the GiN dataset
GiNdata.z08 943MB application/octet-stream .zip archive containing the GiN dataset
readme.txt 4KB text/plain README file containing description of the data
Participant Information_v2_20230612.pdf 82KB application/pdf Participant information sheet
Consent_v2_20230612.pdf 51KB application/pdf Participant consent form

Associated DOIs

Current dataset ...DOIDescription
isReferencedBy 10.1109/OJSP.2023.3344379 Paper describing dataset and its acquisition

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