# Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation

## TL;DR

This paper introduces a Bayesian non-parametric approach for determined blind source separation that automatically adapts to varying source complexities, overcoming limitations of traditional methods like NMF which require manual parameter tuning.

## Contribution

The proposed method employs Bayesian non-parametrics to model sources without needing to specify model complexity, enabling automatic adaptation to real-world source variations.

## Key findings

- Automatically adapts to source complexity
- Eliminates need for parameter tuning in separation
- Outperforms traditional NMF-based methods

## Abstract

This paper proposes a determined blind source separation method using Bayesian non-parametric modelling of sources. Conventionally source signals are separated from a given set of mixture signals by modelling them using non-negative matrix factorization (NMF). However in NMF, a latent variable signifying model complexity must be appropriately specified to avoid over-fitting or under-fitting. As real-world sources can be of varying and unknown complexities, we propose a Bayesian non-parametric framework which is invariant to such latent variables. We show that our proposed method adapts to different source complexities, while conventional methods require parameter tuning for optimal separation.