# Low-complexity acoustic scene classification in DCASE 2022 Challenge

arXiv: https://arxiv.org/abs/2206.03835 · HTML: https://mlsanity.com/paper/2206.03835/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2022.

## Built on by (most cited first, 11 of 11)

- [Device-Robust Acoustic Scene Classification via Impulse Response Augmentation](https://mlsanity.com/paper/2305.07499/) (2023)
- [Continual Learning For On-Device Environmental Sound Classification](https://mlsanity.com/paper/2207.07429/) (2022)
- [Low-complexity CNNs for Acoustic Scene Classification](https://mlsanity.com/paper/2207.11529/) (2022)
- [Dual Knowledge Distillation for Efficient Sound Event Detection](https://mlsanity.com/paper/2402.02781/) (2024)
- [Domain Information Control at Inference Time for Acoustic Scene Classification](https://mlsanity.com/paper/2306.08010/) (2023)
- [Microphone Conversion: Mitigating Device Variability in Sound Event Classification](https://mlsanity.com/paper/2401.06913/) (2024)
- [Unified Microphone Conversion: Many-to-Many Device Mapping via Feature-wise Linear Modulation](https://mlsanity.com/paper/2410.18322/) (2024)
- [Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification](https://mlsanity.com/paper/2503.11363/) (2025)
- [Low-complexity CNNs for Acoustic Scene Classification](https://mlsanity.com/paper/2208.01555/) (2022)
- [Improving Acoustic Scene Classification with City Features](https://mlsanity.com/paper/2503.16862/) (2025)
- [SincDPNet: Interpretable Raw-Waveform Bathroom Activity Recognition for Assistive Living](https://mlsanity.com/paper/2609.34907/) (2026)

## Abstract (arXiv)

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. 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.