Low-Complexity Acoustic Scene Classification in DCASE 2022 Challenge

Irene Martin Morato, Francesco Paissan, Alberto Ancilotto, Toni Heittola, Annamaria Mesaros, Elisabetta Farella, Alessio Brutti, Tuomas Virtanen

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This 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.
Original languageEnglish
Title of host publicationProceedings of the 7th Workshop on Detection and Classication of Acoustic Scenes and Events (DCASE 2022)
EditorsMathieu Lagrange, Annamaria Mesaros, Thomas Pellegrini, Gaël Richard, Romain Serizel, Dan Stowell
ISBN (Electronic)978-952-03-2677-7
Publication statusPublished - 3 Nov 2022
Publication typeA4 Article in conference proceedings
EventWorkshop on Detection and Classification of Acoustic Scenes and Events - Nancy, France
Duration: 3 Nov 20224 Nov 2022


ConferenceWorkshop on Detection and Classification of Acoustic Scenes and Events
Abbreviated titleDCASE
Internet address

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