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 semi-supervised 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 semi-supervised separation for real-time applications. We demonstrate this approach on real-time speech denoising.
KeywordsCurrent Frame Source Separation Nonnegative Matrix Factorization Activation Weight Noise Type
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- 3.Wang, F., Tan, C., König, A.C., Li, P.: Efficient Document Clustering via Online Nonnegative Matrix Factorizations. In: SDM (2011)Google Scholar
- 4.Lefèvre, A., Bach, F., Févotte, C.: Online Algorithms for Nonnegative Matrix Factorization with the Itakura-Saito Divergence. In: WASPAA (2011)Google Scholar