# Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction

arXiv: https://arxiv.org/abs/2301.00448 · HTML: https://mlsanity.com/paper/2301.00448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 1 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [LOCA: LOcal Conformal Autoencoder for standardized data coordinates](https://mlsanity.com/paper/2004.07234/) (2020)
- [Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds](https://mlsanity.com/paper/1402.2683/) (2014)
- [Semi-Supervised Sound Source Localization Based on Manifold Regularization](https://mlsanity.com/paper/1508.03148/) (2015)

## Abstract (arXiv)

Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to reverberation and additive noise. We introduce an unsupervised data-driven approach that exploits the natural structure of the data. Our method builds upon local conformal autoencoders (LOCA) - an offline deep learning scheme for learning standardized data coordinates from measurements. Our experimental setup includes a microphone array that measures the transmitted sound source at multiple locations across the acoustic enclosure. We demonstrate that LOCA learns a representation that is isometric to the spatial locations of the microphones. The performance of our method is evaluated using a series of realistic simulations and compared with other dimensionality-reduction schemes. We further assess the influence of reverberation on the results of LOCA and show that it demonstrates considerable robustness.