Published February 5, 2024 | Version v3
Dataset Open

IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift Development Dataset

  • 1. ROR icon Northwestern Polytechnical University
  • 2. Xi'an Lianfeng Acoustic Technologies Co., Ltd.
  • 3. Institute of Acoustics, Chinese Academy of Sciences
  • 4. ROR icon University of Surrey
  • 5. ROR icon Nanyang Technological University

Description

The CAS 2023 dataset is a large-scale dataset that serves as a foundation for research related to environmental acoustic scenes. The dataset includes 10 common acoustic scenes, with a total duration of over 130 hours (until 15 Dec 2023). Each audio clip is 10 seconds long with metadata about the recording location and timestamp. The dataset was collected by members of the Joint Laboratory of Environmental Sound Sensing at the School of Marine Science and Technology, Northwestern Polytechnical University. The data collection period spanned from April 2023 to September 2023, covering 22 different cities across China. The CAS 2023 dataset was collected using the XS-SN-2BE1 manufactured by Xi'an Lianfeng Acoustic Technologies Co., Ltd (https://www.lfxstek.com/).  

The ICME 2024 Semi-supervised Acoustic Scene Classification under Domain Shift challenge (https://2024.ieeeicme.org/grand-challenge-proposals/, https://ascchallenge.xshengyun.com/) dataset consists of development and evaluation datasets, all derived from the CAS 2023 dataset. The development dataset is about 24 hours including the recordings from 8 cities. We provided scene labels for 20% of the data in the development dataset to allow participants to develop effective semi-supervised methods. In the evaluation dataset, data are selected from 12 cities, with 5 unseen cities specifically chosen to provide a more comprehensive evaluation of submissions under domain shift.

Acoustic scenes (10): Bus, Airport, Metro, Restaurant, Shopping mall, Public square, Urban park, Traffic street, Construction site, Bar

Files

ICME2024_ASC_dev_label.csv

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