  Lack of large-scale note-level labeled data is the major obstacle to singing
transcription from polyphonic music. We address the issue by using pseudo
labels from vocal pitch estimation models given unlabeled data. The proposed
method first converts the frame-level pseudo labels to note-level through pitch
and rhythm quantization steps. Then, it further improves the label quality
through self-training in a teacher-student framework. To validate the method,
we conduct various experiment settings by investigating two vocal pitch
estimation models as pseudo-label generators, two setups of teacher-student
frameworks, and the number of iterations in self-training. The results show
that the proposed method can effectively leverage large-scale unlabeled audio
data and self-training with the noisy student model helps to improve
performance. Finally, we show that the model trained with only unlabeled data
has comparable performance to previous works and the model trained with
additional labeled data achieves higher accuracy than the model trained with
only labeled data.
