We propose a co-training algorithm to detect the singing voice segments from the pop songs. Co-training algorithm leverages compatible and partially uncorrelated information across different features to effectively boost the model from unlabeled data. We adopt this technique to take advantage of abundant unlabeled songs and explore the use of different acoustic features including vibrato, harmonic, attack-decay and MFCC (Mel Frequency Cepstral Coefficients). The proposed algorithm substantially reduces the amount of manual labeling work and computational cost. The experiments are conducted on the database of 94 pop solo songs. We achieve an average error rate of 17% in segment level singing voice detection.