Filter Banks and Audio Coding: Compressing Audio Signals Using Python - Tapa blanda

Acerca del autor

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. At ICASSP 2004 in Montreal he and Prof. Juergen Herre from Fraunhofer IIS together gave a very well attended tutorial about Audio Coding, from which the book idea originated.

De la contraportada

This textbook presents the fundamentals of audio coding, used to compress audio and music signals, using Python programs both as examples to illustrate the principles and for experiments for the reader. Together, these programs then form complete audio coders. The author starts with basic knowledge of digital signal processing (sampling, filtering) to give a thorough introduction to filter banks as used in audio coding, and their design methods. He then continues with the next core component, which are psycho-acoustic models. The author finally shows how to design and implement them. Lastly, the author goes on to describe components for more specialized coders, like the Integer-to-Integer MDCT filter bank, and predictive coding for lossless and low delay coding. Included are Python program examples for each section, which illustrate the principles and provide the tools for experiments.

• Comprehensively explains the fundamentals of filter banks and audio coding;
• Provides Python examples for each principle so that completed audio coders are obtained in the language;
• Includes a suite of classroom materials including exercises, experiments, and examples.