# Blind Source Separation Using Mixtures of Alpha-Stable Distributions

arXiv: https://arxiv.org/abs/1711.04460 · HTML: https://mlsanity.com/paper/1711.04460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

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

- [Sketch and shift: a robust decoder for compressive clustering](https://mlsanity.com/paper/2312.09940/) (2023)

## Abstract (arXiv)

We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions. Complex symmetric alpha-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. However, inference of these models is notoriously hard to perform because their probability density functions do not have a closed-form expression in general. Here, we introduce a novel method for estimating mixture of alpha-stable distributions based on characteristic function matching. We apply this to the blind estimation of binary masks in individual frequency bands from multichannel convolutive audio mixes. We show that the proposed method yields better separation performance than Gaussian-based binary-masking methods.