Source separation and machine learning

著者

• Chien, Jen-Tzung

書誌事項

Source separation and machine learning

Jen-Tzung Chien

Academic Press, an imprint of Elsevier, c2019

• : [pbk.]

注記

Includes bibliographical references (p. 337-347) and index

内容説明・目次

内容説明

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.

目次

Part I Fundamental Theories1. Introduction2. Model-based blind source separation3. Adaptive learning machine Part II Advanced Studies4. Independent component analysis5. Nonnegative matrix factorization6. Nonnegative tensor factorization7. Deep neural network8. Summary and Future Trends

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BB27847308
• ISBN:
  • 9780128177969
• 出版国コード: uk
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: London
• ページ数/冊数: xxx, 353 p.
• 大きさ: 24 cm
• 分類:
  • DC23 : 621.38223
• 件名:
  • LCSH : Blind source separation
  • LCSH : Machine learning