Semi-supervised Audio Classification

Semi-supervised audio classification is a machine learning approach that combines a small amount of labeled data with a large amount of unlabeled data to improve the accuracy and efficiency of audio recognition. This technique optimizes the model training process by leveraging the potential structural information in the unlabeled data, thereby enhancing classification performance while reducing labeling costs. Semi-supervised audio classification has significant applications in speech recognition, emotion analysis, environmental sound monitoring, and other fields, effectively addressing the challenge of annotating large-scale audio datasets.