# Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge

arXiv: https://arxiv.org/abs/2405.10018 · HTML: https://mlsanity.com/paper/2405.10018/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2024.

## Built on by (most cited first, 9 of 9)

- [Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds](https://mlsanity.com/paper/2509.04166/) (2025)
- [Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction](https://mlsanity.com/paper/2409.11964/) (2024)
- [Improving Acoustic Scene Classification with City Features](https://mlsanity.com/paper/2503.16862/) (2025)
- [Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation](https://mlsanity.com/paper/2509.09931/) (2025)
- [An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2509.11168/) (2025)
- [Lightweight and Generalizable Acoustic Scene Representations via Contrastive Fine-Tuning and Distillation](https://mlsanity.com/paper/2510.03728/) (2025)
- [DDSC: Dynamic Dual-Signal Curriculum for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2510.17345/) (2025)
- [Learning from imperfect teachers for low-resource acoustic generalization](https://mlsanity.com/paper/2610.05256/) (2026)
- [A Strength-Monotonic Law for Domain Alignment in Frozen-Embedding Bioacoustic Classification](https://mlsanity.com/paper/2610.09737/) (2026)

## Abstract (arXiv)

This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on recording device mismatches and low-complexity constraints. This year's edition introduces an additional real-world problem: participants must develop data-efficient systems for five scenarios, which progressively limit the available training data. The provided baseline system is based on an efficient, factorized CNN architecture constructed from inverted residual blocks and uses Freq-MixStyle to tackle the device mismatch problem. The task received 37 submissions from 17 teams, with the large majority of systems outperforming the baseline. The top-ranked system's accuracy ranges from 54.3% on the smallest to 61.8% on the largest subset, corresponding to relative improvements of approximately 23% and 9% over the baseline system on the evaluation set.