Korean Pansori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding

Abstract

In this paper, first, we delved into the experiment by comparing various attention mechanisms in the semantic pixel-wise segmentation framework to perform frame-level transcription tasks. Second, the Viterbi algorithm was utilized by transferring the knowledge of the frame-level transcription model to obtain the vocal notes of Korean Pansori. We considered a semantic pixel-wise segmentation framework for frame-level transcription as the source task and a Viterbi algorithm-based Korean Pansori note-level transcription as the target task. The primary goal of this paper was to transcribe the vocal notes of Pansori music, a traditional Korean art form. To achieve this goal, the initial step involved conducting the experiments with the source task, where a trained model was employed for vocal melody extraction. To achieve the desired vocal note transcription for the target task, the Viterbi algorithm was utilized with the frame-level transcription model. By leveraging this approach, we sought to accurately transcribe the vocal notes present in Pansori performances. The effectiveness of our attention-based segmentation methods for frame-level transcription in the source task has been compared with various algorithms using the vocal melody task of the MedleyDB dataset, enabling us to measure the voicing recall, voicing false alarm, raw pitch accuracy, raw chroma accuracy, and overall accuracy. The results of our experiments highlight the significance of attention mechanisms for enhancing the performance of frame-level music transcription models. We also conducted a visual and subjective comparison to evaluate the results of the target task for vocal note transcription. Since there was no ground truth vocal note for Pansori, this analysis provides valuable insights into the preservation and appreciation of this culturally rich art form.