# A Curated Dataset of Urban Scenes for Audio-Visual Scene Analysis

arXiv: https://arxiv.org/abs/2011.00030 · HTML: https://mlsanity.com/paper/2011.00030/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Wav2CLIP: Learning Robust Audio Representations From CLIP](https://mlsanity.com/paper/2110.11499/) (2021)
- [SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping](https://mlsanity.com/paper/2203.16749/) (2022)
- [InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue](https://mlsanity.com/paper/2510.13747/) (2025)
- [Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations](https://mlsanity.com/paper/2303.01664/) (2023)
- [DiTSE: High-Fidelity Generative Speech Enhancement via Latent Diffusion Transformers](https://mlsanity.com/paper/2504.09381/) (2025)
- [Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification](https://mlsanity.com/paper/2301.00145/) (2022)
- [Versatile audio-visual learning for emotion recognition](https://mlsanity.com/paper/2305.07216/) (2023)
- [Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance](https://mlsanity.com/paper/2408.06264/) (2024)
- [Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification](https://mlsanity.com/paper/2107.13180/) (2021)
- [A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification](https://mlsanity.com/paper/2203.04114/) (2022)
- [Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy](https://mlsanity.com/paper/2204.11420/) (2022)
- [Multi-Source Transformer Architectures for Audiovisual Scene Classification](https://mlsanity.com/paper/2210.10212/) (2022)
- [Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol](https://mlsanity.com/paper/2405.00384/) (2024)
- [A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)](https://mlsanity.com/paper/2407.03110/) (2024)
- [Bridging the Gap between Text, Audio, Image, and Any Sequence: A Novel Approach using Gloss-based Annotation](https://mlsanity.com/paper/2410.03146/) (2024)
- [AVSD-Scenes: A Dataset for Audio-Visual Description of Urban Scenes](https://mlsanity.com/paper/2610.01861/) (2026)

## Abstract (arXiv)

This paper introduces a curated dataset of urban scenes for audio-visual scene analysis which consists of carefully selected and recorded material. The data was recorded in multiple European cities, using the same equipment, in multiple locations for each scene, and is openly available. We also present a case study for audio-visual scene recognition and show that joint modeling of audio and visual modalities brings significant performance gain compared to state of the art uni-modal systems. Our approach obtained an 84.8% accuracy compared to 75.8% for the audio-only and 68.4% for the video-only equivalent systems.