# Verse1-Chorus-Verse2 Structure: A Stacked Ensemble Approach for Enhanced Music Emotion Recognition
## 3 Results and Discussion
### Comparison with Other Existing Studies
Furthermore, our bimodal stacked ensemble model demonstrates a substantial performance enhancement, achieving an average of their difference of 12.49% in accuracy. This significant improvement supports the claims of [29; 30; 31] that combining audio and lyric features leads to superior performance in music emotion recognition. The models using lyrics-only features also contribute significantly to the performance in music emotion classification. The study by [36] utilizes [PERSON]'s model of eight basic emotions to classify music based on lyrics, with a relatively low accuracy of 43.4%. This suggests that while the approach may capture some emotional content from the lyrics, it struggles with precise classification, possibly due to the limited size of the dataset and the complexity of accurately mapping words to emotional states. The Bi-LSTM model using \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Dataset** & **Features** & **Evaluation** \\ \hline Genetic Algorithm [35] & 200+ English and & & \\  & Hindi Songs & Audio Only & 84.57\% \\ \hline [PERSON]’s emotion model [36] & 100 songs & Lyrics Only & 43.4\% \\ \hline Bidirectional Long-Short Term & 2189 songs from & & \\ Memory Using GloVe [37] & Moody Lyrics & Lyrics Only & 91.0\% \\ \hline Classification and Regression by & & & \\ quadrant [38] & 180 lyrics & Lyrics Only & 77.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._GloVe word embeddings achieves a high accuracy of 91.0%. This impressive performance highlights the effectiveness of advanced NLP techniques and deep learning models in processing and understanding lyrical content. The large dataset size likely contributes to the robustness and generalizability of the model [37]. Lastly, the model that employs a classification and regression approach using [PERSON]'s emotion model to analyze music lyrics [38] has an accuracy of 77.1%. The model shows solid performance, suggesting that quadrant-based emotional classification can effectively capture the emotional nuances in lyrics. The highest accuracy achieved by a lyrics-only model is 91.0% using a Bidirectional Long-Short Term Memory Model with GloVe embeddings [37]. Our proposed model achieves an accuracy of 96.28%, marking a 5.28% improvement over the best lyrics-only model. Furthermore, our bimodal stacked ensemble model significantly outperforms the lyrics-only model, achieving an average difference of 25.78% in accuracy. This substantial improvement once again supports the claims of [29; 30; 31] that integrating audio and lyric features leads to enhanced performance in music emotion recognition. Our 6-Input bimodal stacked model does outperform the most advanced audio-only, lyrics-only, and fusion-based approaches. The combination of audio and lyrics features, along with the innovative Verse1-Chorus-Verse2 stacking method, contributes to a higher accuracy and robust performance. This comparison clearly indicates the advancements achieved by our model. In comparison with its alignment with current research trends confirms the validity of its underlying principles. Our study contributes to the evolving discourse on MER by reinforcing the merit of composite analyses and the potential of stacking methods in harnessing multimodal information. Further investigation into feature extraction techniques and model architectures is warranted to fully realize the capabilities of bimodal approaches in MER.