In recent decades, the field of signal processing has rapidly evolved due to diverse application demands, leading to a rich array of scientific questions and research areas. The forms of signals, their formation mechanisms, and the information extraction methods vary by application, resulting in diverse signal processing techniques. Common techniques can be categorized into three types: transformation, detection, and filtering. Signal transformation converts signals from their original domain to a more suitable target domain for analysis; signal detection aims to identify the existence of relevant information within a signal and its specific time and location; and signal filtering focuses on extracting or separating source signals of interest from observed signals. In acoustic signal processing, techniques include sound source localization, sound event detection, voiceprint extraction and recognition, noise reduction, and source separation, with applications in speech communication, voice interaction, smart healthcare, and industrial diagnostics. Recently, the advancement of deep learning technologies has shifted methodologies in acoustic signal processing from knowledge-driven to data-driven approaches, leading to significant research outcomes. This paper aims to systematically summarize the principles and methods of data-driven acoustic signal processing, providing a comprehensive understanding framework for academic exploration and practical applications.