Semi-Supervised Audio Regression

Semi-Supervised Audio Regression is a machine learning method that combines a small amount of labeled data with a large amount of unlabeled data to predict audio features. Its goal is to improve the model's generalization ability and prediction accuracy for audio signal regression tasks by leveraging the structural information in the unlabeled data. This method has significant application value in fields such as speech recognition, emotion analysis, and music information retrieval, effectively reducing annotation costs and enhancing system performance.