# Acoustic Scene Classification

arXiv: https://arxiv.org/abs/1411.3715 · HTML: https://mlsanity.com/paper/1411.3715/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2014.

## Built on by (most cited first, 26 of 26)

- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [Describing Differences between Text Distributions with Natural Language](https://mlsanity.com/paper/2201.12323/) (2022)
- [Automated Audio Captioning: An Overview of Recent Progress and New Challenges](https://mlsanity.com/paper/2205.05949/) (2022)
- [A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification](https://mlsanity.com/paper/2203.15751/) (2022)
- [Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2402.02694/) (2024)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds](https://mlsanity.com/paper/2509.04166/) (2025)
- [Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block](https://mlsanity.com/paper/2211.02940/) (2022)
- [Audio Difference Captioning Utilizing Similarity-Discrepancy Disentanglement](https://mlsanity.com/paper/2308.11923/) (2023)
- [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)
- [Audio Attacks and Defenses against AED Systems -- A Practical Study](https://mlsanity.com/paper/2106.07428/) (2021)
- [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)
- [Acoustic scene classification using auditory datasets](https://mlsanity.com/paper/2112.13450/) (2021)
- [Automatic Audio Captioning using Attention weighted Event based Embeddings](https://mlsanity.com/paper/2201.12352/) (2022)
- [1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality](https://mlsanity.com/paper/2204.00555/) (2022)
- [Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy](https://mlsanity.com/paper/2204.11420/) (2022)
- [Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework](https://mlsanity.com/paper/2206.13476/) (2022)
- [Definition-independent Formalization of Soundscapes: Towards a Formal Methodology](https://mlsanity.com/paper/2310.13404/) (2023)
- [Improving Acoustic Scene Classification with City Features](https://mlsanity.com/paper/2503.16862/) (2025)
- [ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification](https://mlsanity.com/paper/2508.15632/) (2025)
- [An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2509.11168/) (2025)
- [DDSC: Dynamic Dual-Signal Curriculum for Data-Efficient Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2510.17345/) (2025)
- [Exploiting Neural Audio Codec Latents for Adversarial Audio Attacks](https://mlsanity.com/paper/2606.20893/) (2026)
- [Symbiotic Architecture for Post-Hoc Audio Extension of Frozen Language Models](https://mlsanity.com/paper/2609.30784/) (2026)
- [SincDPNet: Interpretable Raw-Waveform Bathroom Activity Recognition for Assistive Living](https://mlsanity.com/paper/2609.34907/) (2026)

## Abstract (arXiv)

In this article we present an account of the state-of-the-art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different imple- mentations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The dataset recorded for this purpose is presented, along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods. We use a baseline method that employs MFCCS, GMMS and a maximum likelihood criterion as a benchmark, and only find sufficient evidence to conclude that three algorithms significantly outperform it. We also evaluate the human classification accuracy in performing a similar classification task. The best performing algorithm achieves a mean accuracy that matches the median accuracy obtained by humans, and common pairs of classes are misclassified by both computers and humans. However, all acoustic scenes are correctly classified by at least some individuals, while there are scenes that are misclassified by all algorithms.