# Acoustic Scene Classification: A Competition Review

arXiv: https://arxiv.org/abs/1808.02357 · HTML: https://mlsanity.com/paper/1808.02357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments](https://mlsanity.com/paper/2501.09394/) (2025)
- [Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices](https://mlsanity.com/paper/2110.09598/) (2021)

## Abstract (arXiv)

In this paper we study the problem of acoustic scene classification, i.e., categorization of audio sequences into mutually exclusive classes based on their spectral content. We describe the methods and results discovered during a competition organized in the context of a graduate machine learning course; both by the students and external participants. We identify the most suitable methods and study the impact of each by performing an ablation study of the mixture of approaches. We also compare the results with a neural network baseline, and show the improvement over that. Finally, we discuss the impact of using a competition as a part of a university course, and justify its importance in the curriculum based on student feedback.