# Verse1-Chorus-Verse2 Structure: A Stacked Ensemble Approach for Enhanced Music Emotion Recognition
## 3 Results and Discussion
### Effectiveness of Multi-Input Stacking Methods  
The results from the various analyses of the structural elements of songs (Verse 1, Verse 2, Chorus, Whole Song, and the combined Versel-Chorus-Verse2 structure) using different MinDF settings provide a compelling argument for the bimodal approach and the stacking method in music emotion recognition.  
By utilizing both audio and lyrics data, the bimodal approach captures a more complete picture of the emotional cues present in music. Audio features might capture the tone, beat, and melody, while lyrics provide semantic and narrative context. As seen in the results, models that consider both modes tend to perform better in terms of accuracy and F1 scores, especially when processing the complete structure of Versel-Chorus-Verse2. This indicates that combining these two types of data helps the model in accurately identifyingthe emotions conveyed by a song. Each mode compensates for the potential shortcomings of the other. For instance, where lyrics might be ambiguous, the clarity of emotion could be reinforced by the audio, and vice versa. A bimodal approach can potentially be more robust to songs with less clear emotional expression in one mode. For example, if the lyrics are not strongly indicative of emotion, the audio features can provide the necessary cues to classify the song's emotion correctly. It is then to be more effective if a complete selected dataset is fed into the model.  
Stacking methods, on the other hand, which involve using predictions from individual models as inputs for a final meta-learner, have shown to be effective in the results. This is likely because stacking allows for the model to not only learn from the input data but also from the patterns in the predictions made by the base models. The stacking approach allows for complexity where necessary. For example, the base models can be complex and tuned to capture the nuances in their specific modalities, while the meta-learner can be simpler, focusing on combining these nuances effectively. Since stacking methods use predictions from base models as features, they can reduce the risk of overfitting by distilling the features into more robust meta-features that are less likely to be noise-driven. Stacking inherently benefits from the diversity of models. If the audio and lyrics models make different kinds of errors, stacking can mitigate these through the meta-learner, which learns to correct these errors when combining the models' outputs.  
Based on the results above, the stacking method has shown to be particularly effective for the Verse1-Chorus-Verse2 structure, likely due to its ability to integrate and leverage the unique emotional content presented in each song part. This suggests that for tasks such as music emotion recognition, where the aim is to understand complex and subjective qualities like emotional tone, a stacked bimodal approach is advantageous. It allows the creation of a more nuanced and accurate emotion recognition system by combining the strengths of both audio and lyrics-based features, as opposed to relying on a unimodal approach that might miss out on key emotional indicators.