Acoustic Echo Cancellation Using Gaussian Processes

Jyunichiro, Tomita; Hirai, Yuzo

doi:10.1007/978-3-642-03040-6_43

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…time or spatial position). A successful application of GPs to a subject close to source separation can also be found in [39] for echo cancellation.…”

Section: E Conclusionmentioning

confidence: 99%

Gaussian Processes for Underdetermined Source Separation

Liutkus

Badeau

Richard

2011

IEEE Trans. Signal Process.

130

View full text Add to dashboard Cite

Abstract-Gaussian process (GP) models are very popular for machine learning and regression and they are widely used to account for spatial or temporal relationships between multivariate random variables. In this paper, we propose a general formulation of underdetermined source separation as a problem involving GP regression. The advantage of the proposed unified view is firstly to describe the different underdetermined source separation problems as particular cases of a more general framework. Secondly, it provides a flexible means to include a variety of prior information concerning the sources such as smoothness, local stationarity or periodicity through the use of adequate covariance functions. Thirdly, given the model, it provides an optimal solution in the minimum mean squared error (MMSE) sense to the source separation problem. In order to make the GP models tractable for very large signals, we introduce framing as a GP approximation and we show that computations for regularly sampled and locally stationary GPs can be done very efficiently in the frequency domain. These findings establish a deep connection between GP and Nonnegative Tensor Factorizations with the Itakura-Saito distance and lead to effective methods to learn GP hyperparameters for very large and regularly sampled signals.

show abstract

“…time or spatial position). A successful application of GPs to a subject close to source separation can also be found in [39] for echo cancellation.…”

Section: E Conclusionmentioning

confidence: 99%