  Automatic singing voice understanding tasks, such as singer identification,
singing voice transcription, and singing technique classification, benefit from
data-driven approaches that utilize deep learning techniques. These approaches
work well even under the rich diversity of vocal and noisy samples owing to
their representation ability. However, the limited availability of labeled data
remains a significant obstacle to achieving satisfactory performance. In recent
years, self-supervised learning models (SSL models) have been trained using
large amounts of unlabeled data in the field of speech processing and music
classification. By fine-tuning these models for the target tasks, comparable
performance to conventional supervised learning can be achieved with limited
training data. Therefore, in this paper, we investigate the effectiveness of
SSL models for various singing voice recognition tasks. We report the results
of experiments comparing SSL models for three different tasks (i.e., singer
identification, singing voice transcription, and singing technique
classification) as initial exploration and aim to discuss these findings.
Experimental results show that each SSL model achieves comparable performance
and sometimes outperforms compared to state-of-the-art methods on each task. We
also conducted a layer-wise analysis to further understand the behavior of the
SSL models.
