# Urban Rhapsody: Large-scale exploration of urban soundscapes

arXiv: https://arxiv.org/abs/2205.13064 · HTML: https://mlsanity.com/paper/2205.13064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 97 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 17 of 17)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Embedding Projector: Interactive Visualization and Interpretation of Embeddings](https://mlsanity.com/paper/1611.05469/) (2016)
- [Unsupervised Learning of Semantic Audio Representations](https://mlsanity.com/paper/1711.02209/) (2017)
- [Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes](https://mlsanity.com/paper/1711.01369/) (2017)
- [Audio Spectrogram Representations for Processing with Convolutional Neural Networks](https://mlsanity.com/paper/1706.09559/) (2017)
- [Who calls the shots? Rethinking Few-Shot Learning for Audio](https://mlsanity.com/paper/2110.09600/) (2021)
- [Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data](https://mlsanity.com/paper/2008.13321/) (2020)
- [Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time](https://mlsanity.com/paper/1907.04435/) (2019)
- [Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets](https://mlsanity.com/paper/1610.06978/) (2016)
- [II-20: Intelligent and pragmatic analytic categorization of image collections](https://mlsanity.com/paper/2005.02149/) (2020)
- [Urban Pulse: Capturing the Rhythm of Cities](https://mlsanity.com/paper/1608.06949/) (2016)
- [Human diffusion and city influence](https://mlsanity.com/paper/1501.07788/) (2015)

## Built on by (most cited first, 3 of 3)

- [SideSeeing: A multimodal dataset and collection of tools for sidewalk assessment](https://mlsanity.com/paper/2407.06464/) (2024)
- [Audio-Based Pedestrian Detection in the Presence of Vehicular Noise](https://mlsanity.com/paper/2509.19295/) (2025)
- [VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design](https://mlsanity.com/paper/2508.07497/) (2025)

## Abstract (arXiv)

Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state-of-the-art audio representation, machine learning, and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high-precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five-year deployment of a one-of-a-kind sensor network in New York City.