# Multi-modal Blind Source Separation with Microphones and Blinkies

## TL;DR

This paper introduces a novel blind source separation method combining microphone array data with low-rate sound sensors called blinkies, improving separation performance and robustness over traditional methods.

## Contribution

It presents a joint probabilistic model and an iterative algorithm that leverages both microphone and blinkie measurements for enhanced source separation.

## Key findings

- Median separation performance improved by up to 8 dB over independent vector analysis.
- The algorithm reduces variability in separation results.
- Validation through numerical experiments confirms effectiveness.

## Abstract

We propose a blind source separation algorithm that jointly exploits measurements by a conventional microphone array and an ad hoc array of low-rate sound power sensors called blinkies. While providing less information than microphones, blinkies circumvent some difficulties of microphone arrays in terms of manufacturing, synchronization, and deployment. The algorithm is derived from a joint probabilistic model of the microphone and sound power measurements. We assume the separated sources to follow a time-varying spherical Gaussian distribution, and the non-negative power measurement space-time matrix to have a low-rank structure. We show that alternating updates similar to those of independent vector analysis and Itakura-Saito non-negative matrix factorization decrease the negative log-likelihood of the joint distribution. The proposed algorithm is validated via numerical experiments. Its median separation performance is found to be up to 8 dB more than that of independent vector analysis, with significantly reduced variability.