# Audio scene monitoring using redundant ad-hoc microphone array networks

arXiv: https://arxiv.org/abs/2103.01830 · HTML: https://mlsanity.com/paper/2103.01830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Validating Hyperspectral Image Segmentation](https://mlsanity.com/paper/1811.03707/) (2018)
- [Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals](https://mlsanity.com/paper/1807.11722/) (2018)
- [Semi-supervised source localization with deep generative modeling](https://mlsanity.com/paper/2005.13163/) (2020)
- [Lightweight and Optimized Sound Source Localization and Tracking Methods for Open and Closed Microphone Array Configurations](https://mlsanity.com/paper/1812.00115/) (2018)
- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling](https://mlsanity.com/paper/2009.12858/) (2020)
- [Mirage: 2D Source Localization Using Microphone Pair Augmentation with Echoes](https://mlsanity.com/paper/1906.08968/) (2019)
- [SSLIDE: Sound Source Localization for Indoors based on Deep Learning](https://mlsanity.com/paper/2010.14420/) (2020)

## Built on by (most cited first, 1 of 1)

- [Estimating Visual Information From Audio Through Manifold Learning](https://mlsanity.com/paper/2208.02337/) (2022)

## Abstract (arXiv)

We present a system for localizing sound sources in a room with several ad-hoc microphone arrays. Each circular array performs direction of arrival (DOA) estimation independently using commercial software. The DOAs are fed to a fusion center, concatenated, and used to perform the localization based on two proposed methods, which require only few labeled source locations (anchor points) for training. The first proposed method is based on principal component analysis (PCA) of the observed DOA and does not require any knowledge of anchor points. The array cluster can then perform localization on a manifold defined by the PCA of concatenated DOAs over time. The second proposed method performs localization using an affine transformation between the DOA vectors and the room manifold. The PCA has fewer requirements on the training sequence, but is less robust to missing DOAs from one of the arrays. The methods are demonstrated with five IoT 8-microphone circular arrays, placed at unspecified fixed locations in an office. Both the PCA and the affine method can easily map out a rectangle based on a few anchor points with similar accuracy. The proposed methods provide a step towards monitoring activities in a smart home and require little installation effort as the array locations are not needed.