# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 4 Experimental Results  
### Frame-Level Transcription  
We conducted the experiment of frame-level transcription by comparing self-attention and channel attention mechanisms, assuming the task as a pixel-wise semantic segmentation framework. The different loss functions like binary cross-entropy and focal loss were also compared. We evaluated the effectiveness of our method against state-of-the-art techniques on the MedleyDB [33] dataset, utilizing the same splitting ratio as described in [30; 31; 35]. The network hyper-parameters remained consistent with those outlined in [30]. The evaluation metrics used were voicing recall (VR), voicing false alarm (VFA), raw pitch accuracy (RPA), raw chroma accuracy (RCA), and overall accuracy (OA). Our self-attention-based approach, integrated with focal loss, demonstrated notable results. The one-layer self-attention with focal loss achieved a voicing recall of 68.05%, with an overall accuracy of 78.74%. As we increased the self-attention layers to two and three, a slight decrease in performance was observed, suggesting potential saturation in model capacity. This observation prompted us to explore the reasons behind this trend and assess whether the increase in self-attention layers contributes significantly or not to model effectiveness. Similarly, when employing binary cross-entropy as the loss function, the two-layer self-attention stands out with a high overall accuracy of 80.25%, showcasing the effectiveness of this combination. This notable performance of the two-layer self-attention brings a closer examination between attention mechanisms and loss functions. One potential avenue for this analysis is the impact of self-attention depth on the model's capacity to capture hierarchical dependencies within the data. This raises questions about the trade-offs involved in increasing or decreasing the depth of self-attention layers and how these choices influence the model's ability to generalize. Our channel attention-based methods, integrated with both focal loss and binary cross-entropy consistently exhibited strong performance. Notably, the focal loss variant stands out with an impressive overall accuracy of 80.33%, surpassing the performance of the binary cross-entropy counterpart, which achieved an accuracy of 79.87%. Focal loss is designed to address class imbalance by down-weighting well-classified examples, potentially enhancing the model's focus on challenging instances. Comparing our proposed methods to established approaches, such as DSM [35], [PERSON]'s [31], and SegNet [30], our techniques demonstrate competitive results. While DSM excels in voicing recall, our methods strike a balance, achieving strong performance across multiple metrics. [PERSON]'s method performs admirably, particularly in voicing false alarm, showcasing its distinct strengths. In conclusion, our proposed vocal melody extraction method, leveraging self-attention and channel attention mechanisms, coupled with carefully chosen loss functions, presents promising results on the MedleyDB dataset. The analysis of different configurations provides insights into the strengths and potential areas for improvement, paving the way for advancements in vocal melody extraction techniques. Table 1 below shows the comparison of various F0 estimation methods on the test data of MedleyDB. We also visualize the results of Passori vocal F0 using our best pre-trained model (channel attention + F) and other pitch estimation methods such pYin [36], PyWorld [37], and Segnet [30]. Figure 3 below shows the tracking of F0 visualization of our method along with other pitch estimation methods in one of the audio samples of Passori. The