# SoundCam: A Dataset for Finding Humans Using Room Acoustics

arXiv: https://arxiv.org/abs/2311.03517 · HTML: https://mlsanity.com/paper/2311.03517/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Self-supervised Moving Vehicle Tracking with Stereo Sound](https://mlsanity.com/paper/1910.11760/) (2019)
- [VisualEchoes: Spatial Image Representation Learning through Echolocation](https://mlsanity.com/paper/2005.01616/) (2020)
- [Audio-Visual Floorplan Reconstruction](https://mlsanity.com/paper/2012.15470/) (2020)
- [BatVision: Learning to See 3D Spatial Layout with Two Ears](https://mlsanity.com/paper/1912.07011/) (2019)
- [GWA: A Large High-Quality Acoustic Dataset for Audio Processing](https://mlsanity.com/paper/2204.01787/) (2022)
- [Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images](https://mlsanity.com/paper/1811.09950/) (2018)
- [dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing](https://mlsanity.com/paper/2104.13168/) (2021)
- [PoseKernelLifter: Metric Lifting of 3D Human Pose using Sound](https://mlsanity.com/paper/2112.00216/) (2021)

## Built on by (most cited first, 5 of 5)

- [Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark](https://mlsanity.com/paper/2403.18821/) (2024)
- [Hearing Anything Anywhere](https://mlsanity.com/paper/2406.07532/) (2024)
- [Hearing Anywhere in Any Environment](https://mlsanity.com/paper/2504.10746/) (2025)
- [BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds](https://mlsanity.com/paper/2503.00389/) (2025)
- [Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score](https://mlsanity.com/paper/2607.01527/) (2026)

## Abstract (arXiv)

A room's acoustic properties are a product of the room's geometry, the objects within the room, and their specific positions. A room's acoustic properties can be characterized by its impulse response (RIR) between a source and listener location, or roughly inferred from recordings of natural signals present in the room. Variations in the positions of objects in a room can effect measurable changes in the room's acoustic properties, as characterized by the RIR. Existing datasets of RIRs either do not systematically vary positions of objects in an environment, or they consist of only simulated RIRs. We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms publicly released to date. It includes 5,000 10-channel real-world measurements of room impulse responses and 2,000 10-channel recordings of music in three different rooms, including a controlled acoustic lab, an in-the-wild living room, and a conference room, with different humans in positions throughout each room. We show that these measurements can be used for interesting tasks, such as detecting and identifying humans, and tracking their positions.