phy696 Advanced Topics Speech and Audio Processing (Complete module description)

Basic principles of signal processing (preferably successfully completed the course Signal- und Systemtheorie and/or Blockpraktikum Digitale Signalverarbeitung)

The students will acquire in-depth knowledge in the field of speech and audio processing. The practical component of the course provides insight into the key properties of the covered methods through a self-study approach, while the implementation of algorithms on a computer supports the application and transfer of theoretical concepts to practical problems.

After reviewing the basic principles of speech processing and statistical signal processing, including adaptive filtering and estimation theory, this course covers techniques and underlying algorithms that are essential for many modern speech communication and audio processing systems, such as mobile phones, smart speakers, hearing aids, and headphones. These include acoustic echo and feedback cancellation, noise reduction, dereverberation, microphone and loudspeaker array processing, and active noise control. The course addresses both algorithms based on statistical signal processing and approaches based on deep learning. During the exercises, a typical hands-free speech communication or audio processing system is implemented in Matlab or Python.

• J. Benesty, M. M. Sondhi, Y. Huang: Handbook of Speech Processing, Springer, 2008.;
• P. Vary, R. Martin: Digital Speech Transmission, Wiley, 2006.;
• P. Loizou: Speech Enhancement: Theory and Practice, CRC Press, 2007.;
• S. Vaseghi: Advanced Digital Signal Processing and Noise Reduction, Wiley, 2006.;
• S. Haykin: Adaptive Filter Theory, Prentice Hall, 2013.

Module Capacity:
unrestricted