Unsupervised Domain Adaptation for Music Transcription: Exploiting Cross-Version Consistency

With the omnipresence of deep learning approaches in modern signal processing, the generalization of models to unseen domains is of crucial importance. Various strategies have been proposed to address such unsupervised domain adaptation including reconstruction autoencoders and adversarial training. In this paper, we propose another strategy using teacher–student learning, where we train a student model for the target domain using pseudo-labels generated by a teacher model trained on the source domain. To improve the adaptation, we propose to exploit cross-version data, i.e., target-domain data without labels but existing in different versions, which are expected to share the same labels. We process the pseudo-labels from the teacher model by keeping only consistent labels or by interpolating conflicting labels. In this paper, we demonstrate the proposed strategy for a music transcription subtask (multi-pitch estimation) using different performances of the same composition. We compare this strategy with a baseline method using a reconstruction autoencoder. Furthermore, we combine our strategy with the baseline method by enforcing the cross-version consistency of the target domain predictions. Our results show that our strategy helps to improve the unsupervised domain adaptation from one instrument (piano) to other instruments (singing and orchestra). Especially for the difficult transfer from piano music to complex orchestral music, we obtain substantial improvements using the proposed approach.