Unsupervised Adaptive Filtering, Volume 1: Blind Source Separation

Unsupervised Adaptive Filtering, Volume 1: Blind Source Separation

Price : 9.69

Ends on : N/A

View on eBay
Unsupervised Adaptive Filtering, Volume 1: Blind Source Separation

Blind source separation is a powerful technique for extracting individual sources from a mixture of signals without any prior knowledge about the sources or the mixing process. In this first volume of our series on unsupervised adaptive filtering, we delve into the principles and applications of blind source separation.

We start by discussing the basic concepts of blind source separation, including the assumptions and constraints involved in separating mixed signals. We then explore various algorithms and approaches for blind source separation, such as independent component analysis (ICA), sparse component analysis (SCA), and non-negative matrix factorization (NMF).

Throughout the volume, we provide practical examples and case studies to illustrate the effectiveness of blind source separation in real-world scenarios. From audio signal processing to biomedical imaging, blind source separation has a wide range of applications and can significantly enhance the quality and accuracy of signal processing tasks.

Whether you are a researcher, engineer, or student interested in signal processing and machine learning, this volume on blind source separation will provide you with a comprehensive overview of the topic and equip you with the knowledge and tools to apply these techniques in your own projects. Stay tuned for more volumes in our series on unsupervised adaptive filtering!
#Unsupervised #Adaptive #Filtering #Volume #Blind #Source #Separation