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2012
Springer

Online PLCA for Real-Time Semi-supervised Source Separation

8 years 2 months ago
Online PLCA for Real-Time Semi-supervised Source Separation
Non-negative spectrogram factorization algorithms such as probabilistic latent component analysis (PLCA) have been shown to be quite powerful for source separation. When training data for all of the sources are available, it is trivial to learn their dictionaries beforehand and perform supervised source separation in an online fashion. However, in many real-world scenarios (e.g. speech denoising), training data for one of the sources can be hard to obtain beforehand (e.g. speech). In these cases, we need to perform semi-supervised source separation and learn a dictionary for that source during the separation process. Existing semisupervised separation approaches are generally offline, i.e. they need to access the entire mixture when updating the dictionary. In this paper, we propose an online approach to adaptively learn this dictionary and separate the mixture over time. This enables us to perform online semisupervised separation for real-time applications. We demonstrate this approac...
Zhiyao Duan, Gautham J. Mysore, Paris Smaragdis
Added 24 Apr 2012
Updated 24 Apr 2012
Type Journal
Year 2012
Where ICA
Authors Zhiyao Duan, Gautham J. Mysore, Paris Smaragdis
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