# Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems

arXiv: https://arxiv.org/abs/2105.13734 · HTML: https://mlsanity.com/paper/2105.13734/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2021.

## Built on by (most cited first, 14 of 14)

- [A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification](https://mlsanity.com/paper/2203.15751/) (2022)
- [Device-Robust Acoustic Scene Classification via Impulse Response Augmentation](https://mlsanity.com/paper/2305.07499/) (2023)
- [QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design](https://mlsanity.com/paper/2206.13909/) (2022)
- [Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization](https://mlsanity.com/paper/2111.06531/) (2021)
- [Efficient CNNs via Passive Filter Pruning](https://mlsanity.com/paper/2304.02319/) (2023)
- [A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification](https://mlsanity.com/paper/2107.01461/) (2021)
- [E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks](https://mlsanity.com/paper/2305.18665/) (2023)
- [Efficient Similarity-based Passive Filter Pruning for Compressing CNNs](https://mlsanity.com/paper/2210.17416/) (2022)
- [Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification](https://mlsanity.com/paper/2209.05900/) (2022)
- [Compressing audio CNNs with graph centrality based filter pruning](https://mlsanity.com/paper/2305.03391/) (2023)
- [Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint](https://mlsanity.com/paper/2107.14658/) (2021)
- [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)
- [Learning from imperfect teachers for low-resource acoustic generalization](https://mlsanity.com/paper/2610.05256/) (2026)

## Abstract (arXiv)

This paper presents the details of Task 1A Acoustic Scene Classification in the DCASE 2021 Challenge. The task targeted development of low-complexity solutions with good generalization properties. The provided baseline system is based on a CNN architecture and post-training quantization of parameters. The system is trained using all the available training data, without any specific technique for handling device mismatch, and obtains an overall accuracy of 47.7%, with a log loss of 1.473. The task received 99 submissions from 30 teams, and most of the submitted systems outperformed the baseline. The most used techniques among the submissions were residual networks and weight quantization, with the top systems reaching over 70% accuracy, and log loss under 0.8. The acoustic scene classification task remained a popular task in the challenge, despite the increasing difficulty of the setup.