Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation
This addresses the issue of model complexity specification in source separation for signal processing applications, though it appears incremental as it builds on existing NMF frameworks.
The paper tackles the problem of blind source separation by proposing a Bayesian non-parametric method that adapts to varying source complexities without parameter tuning, showing it outperforms conventional methods like NMF that require manual adjustment.
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.