Multirate Signal Processing with Examples in Python

Gerald Schuller

ISBN 10: 3032172004 / ISBN 13: 9783032172006

Published by Springer Nature Switzerland AG Jun 2026, 2026

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook provides a comprehensive understanding of multirate signal processing, focusing on practical applications and real-world examples implemented in Python. The book covers fundamental and advanced topics such as filter banks, sampling theory, and z-domain analysis, making it ideal for graduate-level courses and professionals. Through a combination of theoretical insights and Python-based examples, readers gain both the conceptual understanding and practical skills needed to apply multirate techniques in fields like audio coding, telecommunications, and machine learning. Ancillaries include homework problems, Python code examples, a Github repository with Colab Not Elektronisches Buch, and a chatbot for asking questions and finding answers quickly.Offers a detailed, application-focused guide to multirate signal processing, with examples implemented in Python;Covers advanced applications in audio, telecommunications, and machine learning;Includes Python code examples, a book chatbot, and a Github repository with Colab Not Elektronisches Buch. 144 pp. Englisch.

Seller Inventory # 9783032172006

Bibliographic Details

• Title: Multirate Signal Processing with Examples in Python
• Publisher: Springer Nature Switzerland AG Jun 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 3032172004
• ISBN 13: 9783032172006
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 15D millimeters 241 millimeters width by 160 millimeters height by 15 millimeters depth
• Weight: 416 grams

About this title

Synopsis

This textbook provides a comprehensive understanding of multirate signal processing, focusing on practical applications and real-world examples implemented in Python. The book covers fundamental and advanced topics such as filter banks, sampling theory, and z-domain analysis, making it ideal for graduate-level courses and professionals. Through a combination of theoretical insights and Python-based examples, readers gain both the conceptual understanding and practical skills needed to apply multirate techniques in fields like audio coding, telecommunications, and machine learning. Ancillaries include homework problems, Python code examples, a Github repository with Colab Notebooks, and a chatbot for asking questions and finding answers quickly.

• Offers a detailed, application-focused guide to multirate signal processing, with examples implemented in Python;
• Covers advanced applications in audio, telecommunications, and machine learning;
• Includes Python code examples, a book chatbot, and a Github repository with Colab Notebooks.

About the Author

Gerald Schuller is a full professor at the Institute for Media Technology of the Technical University of Ilmenau, since 2008. He was head of the Audio Coding for Special Applications group of the Fraunhofer Institute for Digital Media Technology in Ilmenau, Germany, since January 2002 until 2008, and is now a member of Fraunhofer IDMT. Before joining the Fraunhofer Institute, he was a Member of Technical Staff at Bell Laboratories, Lucent Technologies, and Agere Systems, a Lucent Spin-off, from 1998 to 2001. There he worked in the Multimedia Communications Research Laboratory. He received his Diplom degree in Electrical Engineering from the Technical University of Berlin in 1989, and his Ph.D. (Dr.-Ing.) degree from the University of Hanover in 1997, studied at the Massachusetts Institute of Technology in 1989/90 and at the Georgia Institute of Technology in 1993. He was Associate Editor of the IEEE Transactions on Speech and Audio Processing from 2002 until 2006, and the IEEE Transactions on Signal Processing from 2006 to 2009, and of the IEEE Transactions on Multimedia since 2008. He is recipient of the 2006 IEEE Best Paper Award in the Audio and Electroacoustics Area. His research interests are in filter banks, audio coding, music signal processing, and deep learning for multimedia.