Introduction to General Audio Signal Processing with Machine Learning

This chapter provides a comprehensive introduction to the field of audio signal processing, tracing its historical evolution, defining its core concepts, and outlining its expanding role in modern artificial intelligence applications. Beginning with a technical overview of analog and digital audio signals, the chapter explores fundamental processing techniques in both time and frequency domains. It then chronicles key milestones in the history of the field—from the analog era and digital revolution to the rise of deep learning and the recent shift toward self-supervised and multimodal approaches. A typology of audio signals is presented, highlighting the diverse sources, structures, and application-specific challenges of music, environmental, biomedical, and synthetic audio. The chapter also delineates the relationship between traditional signal processing and the more cognitive, perception-driven goals of computer audition, setting the stage for the integration of machine learning and deep neural networks into auditory tasks. Finally, the chapter outlines the organization and scope of the book, which bridges classical techniques and contemporary deep learning strategies across core methodologies, practical applications, and emerging trends. This serves as a foundational entry point for readers aiming to engage with the evolving landscape of intelligent audio processing systems.