Self-Supervised Sound Classification

Self-Supervised Sound Classification is a technique that leverages unannotated audio data for self-supervised learning, aiming to automatically construct sound classification models by mining the internal structure and features of the data. This method can effectively reduce the reliance on large amounts of labeled data, enhancing the model's generalization and robustness. It is widely applied in areas such as environmental sound monitoring, speech recognition, and music classification.