# Multiple Sound Source Localization with SVD-PHAT

## TL;DR

This paper presents an improved SVD-PHAT method for localizing multiple sound sources more accurately and efficiently, suitable for real-time applications, by projecting observations onto orthogonal subspaces.

## Contribution

It introduces a modified phase transform using SVD for better multi-source localization with low computational complexity.

## Key findings

- Achieves up to 0.0395 radians reduction in RMSE compared to SRP-PHAT
- Enhances localization accuracy for multiple sound sources
- Maintains low algorithm complexity for real-time use

## Abstract

This paper introduces a modification of phase transform on singular value decomposition (SVD-PHAT) to localize multiple sound sources. This work aims to improve localization accuracy and keeps the algorithm complexity low for real-time applications. This method relies on multiple scans of the search space, with projection of each low-dimensional observation onto orthogonal subspaces. We show that this method localizes multiple sound sources more accurately than discrete SRP-PHAT, with a reduction in the Root Mean Square Error up to 0.0395 radians.