| Description |
1 online resource |
|
text txt rdacontent |
|
computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Online resource; title from PDF title page (EBSCO, viewed October 24, 2018). |
| Summary |
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. |
| Subject |
Blind source separation.
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Machine learning.
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Séparation aveugle de sources (Traitement du signal)
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Apprentissage automatique.
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TECHNOLOGY & ENGINEERING -- Mechanical.
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Blind source separation
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Machine learning
|
| ISBN |
9780128045770 (electronic bk.) |
|
0128045779 (electronic bk.) |
|
9780128177969 (electronic bk.) |
|
0128177969 (electronic bk.) |
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9780128045664 |
| Standard No. |
AU@ 000064393752 |
|
AU@ 000065196029 |
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AU@ 000066293712 |
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AU@ 000068133556 |
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AU@ 000068656093 |
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UKMGB 019094303 |
|
