Detailed info

Low-Complexity Acoustic Scene Classification in DCASE 2022 Challenge

AuthorsIrene Martín-Morató, Francesco Paissan, Alberto Ancilotto, Toni Heittola, Annamaria Mesaros, Elisabetta Farella, Alessio Brutti, and Tuomas Virtanen
TitleLow-Complexity Acoustic Scene Classification in DCASE 2022 Challenge
AbstractThis paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 2022 Challenge. The task was a continuation from the previous years, but the low-complexity requirements were changed to the following: the maximum number of allowed parameters, including the zero-valued ones, was 128 K, with parameters being represented using INT8 numerical format; and the maximum number of multiply-accumulate operations at inference time was 30 million. Despite using the same previous year dataset, the audio samples have been shortened to 1 second instead of 10 second for this year challenge. The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46.5 K parameters, and 29.23 million multiply-and-accumulate operations (MMACs). Its performance on the evaluation data is 44.2% accuracy and 1.532 log-loss. In comparison, the top system in the challenge obtained an accuracy of 59.6% and a log loss of 1.091, having 121 K parameters and 28 MMACs. The task received 48 submissions from 19 different teams, most of which outperformed the baseline system.
ISBN978-952-03-2677-7
Conference7th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2022)
Date03/11/2022
LocationNancy, France
Year of Publication2022
UrlTBA
DOITBA
Available Source Code:https://github.com/marmoi/dcase2022_task1_baseline
Open Data used for this publication:

https://zenodo.org/record/6337421

https://zenodo.org/record/6591203

Key Facts

  • Project Coordinator: Dr. Sotiris Ioannidis
  • Institution: Foundation for Research and Technology Hellas (FORTH)
  • E-mail: marvel-info@marvel-project.eu 
  • Start: 01.01.2021
  • Duration: 36 months
  • Participating Organisations: 17
  • Number of countries: 12

Get Connected

Funding

eu FLAG

This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.