Few-Shot Learning for Audio Signal

Few-Shot Learning (FSL) has emerged as a crucial paradigm for audio signal processing, where data scarcity and class imbalance are prevalent challenges. Inspired by the human ability to learn from limited experience, FSL enables machine learning models to generalize effectively from only a few labeled audio samples. This chapter introduces the fundamental concepts of FSL within the context of audio analysis, beginning with a formal problem definition of few-shot classification. We explore the principles of meta-learning—learning to learn across tasks—and its application to FSL through metric-based, optimization-based, and model-based strategies. Additionally, we examine non-meta-learning approaches such as transfer learning, one-shot, zero-shot, and cross-domain learning, emphasizing their relevance and adaptations for audio domains. Drawing insights from cognitive neuroscience and practical examples, we highlight how FSL mimics human learning processes to overcome the limitations of data-hungry deep models in audio applications. The chapter also discusses the role of FSL in enabling robust performance on novel acoustic tasks using minimal data, thereby paving the way for more flexible, efficient, and human-like audio processing systems.