Incremental Learning for Audio Signal

Incremental learning is an emerging framework in machine learning that enables models to continuously learn from new data without forgetting previously acquired knowledge. This chapter explores the foundations, methodologies, and challenges of applying incremental learning to audio tasks. We begin by defining the problem and discussing various scenarios where incremental learning is essential. Next, we introduce key incremental learning frameworks, including regularization-based methods, which constrain model updates to prevent catastrophic forgetting; replay-based methods, which retain and revisit past data to maintain knowledge consistency; and architecture-based approaches, which modify model structures dynamically to accommodate new information. Then, we discuss key evaluation metrics, such as accuracy retention, forgetting rate, and model adaptability, which help assess the effectiveness of different approaches in real-world applications. The final sections cover applications and challenges. By the end of this chapter, readers will have a comprehensive understanding of incremental learning strategies for audio signal processing, equipping them with the necessary knowledge to develop adaptive and scalable audio applications.