1 research outputs found
Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation
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.Comment: 5 pages, 2 figures. Accepted at ICASSP 201