# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 5 Conclusions  
In conclusion, this paper presents a comprehensive exploration of frame-level transcription for vocal melody extraction using attention mechanisms in the source task, followed by the application of the Viterbi algorithm for note-level transcription in the context of Korean Passori. Leveraging knowledge from the source task, a pre-trained model is employed for fundamental frequency (F0) estimation, crucial for the subsequent vocal note transcription in Passori. The Viterbi decoding technique is employed to decode the vocal notes in Passori, addressing the challenge of the absence of note-level ground truth labels for this traditional art form. The transition matrix, capturing the probabilities of transitioning between different vocal states, and the observation matrix, assessing the likelihood of specific vocal events, are constructed. By employing the Viterbi algorithm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Channel Attention + F (Our)**} \\ \hline
**Audio** & **Expert1** & **Expert2** & **Expert3** & **Expert4** & **Expert5** & **Average** \\ \hline chunk0 & 4 & 5 & 3 & 2 & 3 & 3.4 \\ \hline chunk1 & 2 & 3 & 2 & 3 & 2 & 2.4 \\ \hline chunk4 & 3 & 4 & 2 & 2 & 2 & 2.6 \\ \hline chunk5 & 2 & 3 & 4 & 3 & 3 & 3 \\ \hline chunk6 & 3 & 4 & 2 & 2 & 3 & 2.8 \\ \hline chunk7 & 3 & 3 & 2 & 1 & 2 & 2.2 \\ \hline chunk8 & 2 & 2 & 3 & 1 & 2 & 2 \\ \hline chunk9 & 2 & 2 & 2 & 1 & 3 & 2 \\ \hline chunk11 & 2 & 2 & 2 & 2 & 2 & 2 \\ \hline chunk12 & 1 & 2 & 1 & 1 & 1 & 1.2 \\ \hline chunk13 & 3 & 4 & 3 & 3 & 2 & 3 \\ \hline chunk14 & 3 & 4 & 2 & 3 & 3 & 3 \\ \hline chunk16 & 1 & 1 & 2 & 2 & 1 & 1.4 \\ \hline chunk17 & 2 & 2 & 2 & 2 & 1 & 1.8 \\ \hline chunk18 & 1 & 3 & 2 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Note-level subjective evaluation of [PERSON] vocal based on F0 estimation from channel attention + F.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**pYin**} \\ \hline
**Audio** & **Expert1** & **Expert2** & **Expert3** & **Expert4** & **Expert5** & **Average** \\ \hline chunk9 & 3 & 3 & 2 & 3 & 2 & 2.6 \\ \hline chunk11 & 3 & 5 & 2 & 3 & 1 & 2.8 \\ \hline chunk12 & 4 & 4 & 2 & 3 & 1 & 2.8 \\ \hline chunk13 & 2 & 2 & 4 & 3 & 2 & 2.6 \\ \hline chunk14 & 3 & 2 & 1 & 2 & 1 & 1.8 \\ \hline chunk16 & 4 & 5 & 3 & 3 & 1 & 3.2 \\ \hline chunk17 & 3 & 2 & 3 & 3 & 1 & 2.4 \\ \hline chunk18 & 2 & 2 & 2 & 3 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._with these matrices, this paper achieves the goal of identifying the most likely sequence of hidden states representative of Pansori vocal notes. The presented results, including the comparison of attention mechanisms and the evaluation of our methodology against other state-of-the-art methods, highlight the effectiveness and comparability of our approach in the intricate task of Pansori vocal note transcription.  
In summary, this research not only contributes to the advancement of frame-level transcription models but also addresses the unique challenges posed by the transcription of traditional music forms like Pansori. The proposed methodology lays a foundation for preserving and appreciating the rich cultural heritage embedded in Pansori music, offering a valuable tool for future research and exploration of this traditional Korean art form.  
The first author, [PERSON], contributed to the whole project, which includes conceptualization, methodology, validation, analysis, and writing the original draft. The corresponding author, [PERSON], contributed to funding acquisition, conceptualization, and supervision. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Research Foundation of Korea (NRF) under the Development of AI for Analysis and Synthesis of Korean Pansori Project (NRF-2021R1A2C2006895).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.