Self-supervised Learning for Audio Signal

Self-supervised learning has emerged as a transformative approach in audio signal processing, enabling models to learn meaningful representations from large volumes of unlabeled data. This chapter provides an in-depth exploration of self-supervised learning methods applied to audio, focusing on their theoretical foundations, common pretext tasks, and key model architectures. We examine the principles behind self-supervised learning, including the use of contrastive learning, masked prediction, and generative approaches. The chapter also highlights prominent models such as Wav2Vec2, VQ-VAE, AudioCLIP, AudioMAE, and SSAMBA, demonstrating their effectiveness in a variety of audio applications such as speech recognition, music information retrieval, and environmental sound classification. Additionally, the challenges associated with self-supervised learning in the audio domain, including data quality, evaluation metrics, and scalability, are discussed. This chapter concludes by outlining potential future directions for research, with an emphasis on improving model generalization and expanding the applicability of self-supervised learning methods in diverse real-world scenarios.