# A multi-device dataset for urban acoustic scene classification

arXiv: https://arxiv.org/abs/1807.09840 · HTML: https://mlsanity.com/paper/1807.09840/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2018.

## Built on by (most cited first, 30 of 75)

- [GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://mlsanity.com/paper/2406.11768/) (2024)
- [OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM](https://mlsanity.com/paper/2510.15870/) (2025)
- [Automated Audio Captioning: An Overview of Recent Progress and New Challenges](https://mlsanity.com/paper/2205.05949/) (2022)
- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks](https://mlsanity.com/paper/2110.07313/) (2021)
- [Real-Time Target Sound Extraction](https://mlsanity.com/paper/2211.02250/) (2022)
- [Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2105.12395/) (2021)
- [DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction](https://mlsanity.com/paper/2310.04567/) (2023)
- [ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds](https://mlsanity.com/paper/2409.09213/) (2024)
- [SubSpectral Normalization for Neural Audio Data Processing](https://mlsanity.com/paper/2103.13620/) (2021)
- [A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification](https://mlsanity.com/paper/2203.15751/) (2022)
- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer](https://mlsanity.com/paper/2409.08425/) (2024)
- [Robust Time Series Denoising with Learnable Wavelet Packet Transform](https://mlsanity.com/paper/2206.06126/) (2022)
- [MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions](https://mlsanity.com/paper/2105.02702/) (2021)
- [Simple Pooling Front-ends For Efficient Audio Classification](https://mlsanity.com/paper/2210.00943/) (2022)
- [IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research](https://mlsanity.com/paper/2104.13620/) (2021)
- [Learning Representations for New Sound Classes With Continual Self-Supervised Learning](https://mlsanity.com/paper/2205.07390/) (2022)
- [NoreSpeech: Knowledge Distillation based Conditional Diffusion Model for Noise-robust Expressive TTS](https://mlsanity.com/paper/2211.02448/) (2022)
- [DECAR: Deep Clustering for learning general-purpose Audio Representations](https://mlsanity.com/paper/2110.08895/) (2021)
- [Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift](https://mlsanity.com/paper/2402.02694/) (2024)
- [Say No to Freeloader: Protecting Intellectual Property of Your Deep Model](https://mlsanity.com/paper/2408.13161/) (2024)
- [Anomalous Sound Detection with Machine Learning: A Systematic Review](https://mlsanity.com/paper/2102.07820/) (2021)
- [Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks](https://mlsanity.com/paper/2201.03386/) (2022)
- [Efficient CNNs via Passive Filter Pruning](https://mlsanity.com/paper/2304.02319/) (2023)
- [An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network](https://mlsanity.com/paper/2104.02523/) (2021)
- [Improving weakly supervised sound event detection with self-supervised auxiliary tasks](https://mlsanity.com/paper/2106.06858/) (2021)
- [A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification](https://mlsanity.com/paper/2107.01461/) (2021)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [SLICER: Learning universal audio representations using low-resource self-supervised pre-training](https://mlsanity.com/paper/2211.01519/) (2022)

## Abstract (arXiv)

This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task. As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acoustic variability than the previous datasets used for this task, and in addition to high-quality binaural recordings, it also includes data recorded with mobile devices. We also present the baseline system consisting of a convolutional neural network and its performance in the subtasks using the recommended cross-validation setup.