Machine Learning Methods for Audio Signal

This chapter provides a comprehensive overview of various machine learning paradigms and their applications in audio signal processing. We begin by introducing the fundamental learning paradigms, including supervised learning, unsupervised learning, self-supervised learning, and semi-supervised learning, outlining their respective roles in audio-related tasks. Next, we explore traditional machine learning methods, detailing widely used classification, regression, and clustering algorithms that have historically been applied to speech, music, and environmental sound analysis. Although these approaches remain relevant, they are increasingly complemented or replaced by deep learning techniques that offer greater scalability and performance for complex tasks. The chapter then turns to deep neural networks, which have transformed audio processing through automatic feature extraction and end-to-end learning. Key architectures, including CNNs, RNNs, transformers, and generative models, are introduced with a focus on their impact across tasks such as speech recognition, audio classification, source separation, and music generation. The final section explores emerging applications of deep reinforcement learning in adaptive audio synthesis, intelligent noise suppression, and interactive sound generation. By the end of the chapter, readers will grasp a broad spectrum of machine learning techniques in audio, from classical methods to advanced deep learning models that continue to shape the field.