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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Source Separation and Machine Learning (Paperback)
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Source Separation and Machine Learning (Paperback)
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Source Separation and Machine Learning presents the fundamentals in
adaptive learning algorithms for Blind Source Separation (BSS) and
emphasizes the importance of machine learning perspectives. It
illustrates how BSS problems are tackled through adaptive learning
algorithms and model-based approaches using the latest information
on mixture signals to build a BSS model that is seen as a
statistical model for a whole system. Looking at different models,
including independent component analysis (ICA), nonnegative matrix
factorization (NMF), nonnegative tensor factorization (NTF), and
deep neural network (DNN), the book addresses how they have evolved
to deal with multichannel and single-channel source separation.
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