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.
Code (0)
등록된 구현이 없습니다.
Tasks
blind source separationSimilar Papers 제목 키워드 기반
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonpa…
MULTI-VIEW LEARNINGNonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesia…
Topic ModelsBayesian Nonparametric Modeling of Heterogeneous Groups of Censored Data
Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics. This presents problems as they cannot be pooled directly due to their hetero…
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets. Unfortunately, Bayesian inference methods often require high-dimensional averages and can be slow to compute, es…
Bayesian InferenceConstruction of Nonparametric Bayesian Models from Parametric Bayes Equations
We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem has generated much inte…