Singing Voice Detection via Similarity-Based Semi-Supervised Learning

Data-driven methods play an important role in Singing Voice Detection (SVD). However, datasets with precise annotations are scarce. In this paper, we propose an SVD method via similarity-based semi-supervised learning (SSSL_SVD). For one thing, we propose to enrich the diversity of training data using the self-training semi-supervised method (SSL). In SSL, pseudo labels of the unlabeled data are first generated by a pre-trained teacher model and are then used to train a student model. For another thing, we propose to measure the audio frame from a similarity-based perspective. Taking it into consideration, we could provide more appropriate learning targets. Finally, experiment results indicate that the proposed method achieved comparable results with state-of-the-art (SOTA) algorithms.