Starting with essential maths, fundamentals of signals and systems, and classical concepts of DSP, this book presents, from an application-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements, filter design, codecs, machine learning for audio pattern recognition and machine audition, spatial audio, array technologies and hearing aids. Some research outcomes are fed into book as worked examples. As a research informed text, the book attempts to present DSP and machine learning from a new and more relevant angle to acousticians and audio engineers.
Some MATLAB® codes or frameworks of algorithms are given as downloads available on the CRC Press website. Suggested exploration and mini project ideas are given for "proof of concept" type of exercises and directions for further study and investigation. The book is intended for researchers, professionals, and senior year students in the field of audio acoustics.
Audio Signals and Audio Systems. Sampling Quantization and Discrete Fourier. DSP in Acoustical Transfer Function Measurements. Digital Filters and z-Transform. Audio Codecs. DSP in Binaural Hearing and Microphone Arrays. Adaptive Filters. Machine Learning in Acoustic DSP. Unsupervised Learning and Blind Source Separation. DSP in Hearing Aids.