# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 4 Experiments
### Results  
Based on the evaluation metrics described earlier, we present the results of experiments using two objective scores--CLAP and KAD--as well as a subjective assessment using MOS, to evaluate the performance of the AudioLDM model with integrated LoRA under various parameter settings. The CLAP and KAD scores were recorded at five training checkpoints using different combinations of rank and alpha values. For each checkpoint, we generated audio five times with different random seeds and reported the average score. The MOS scores were collected through a user study involving 45 participants from the general public. Furthermore, to facilitate a comprehensive comparison, the proposed method was evaluated alongside AudioLDM and a fully fine-tuned MusicGen under identical experimental conditions. This evaluation allowed for a thorough assessment of the effectiveness of LoRA integration in terms of both objective performance and subjective perceptual quality. Table 6 shows the CLAP scores for each rank and alpha configuration. In all settings, the scores ranged mostly between 0.57 and 0.69. The highest score, 0.6995, was achieved with rank = 8 and alpha = 16 at 800 epochs, while the lowest score, 0.5717, appeared in the rank = 1, alpha = 1 configuration at 400 epochs. These results suggest that increasing rank and alpha tends to improve CLAP performance, indicating better alignment between the generated audio and the input text prompt. Table 7 shows the KAD scores for each rank and alpha configuration. Lower scores indicate better alignment with the reference audio. The lowest score, 0.2906, was observed for rank = 1, alpha = 1 at 200 epochs, suggesting a strong similarity to reference audio. In contrast, the highest score, 0.9936, was observed for rank = 8, alpha = 8 at 400 epochs, indicating a substantial deviation from the reference audio. Overall, configurations with larger rank and alpha values tended to result in higher KAD scores, implying a trade-off between semantic alignment and audio naturalness or consistency. To evaluate practical performance differences in music generation, we compared the CLAP and KAD scores of the proposed method with those of the original AudioLDM and a fully fine-tuned MusicGen model on the same dataset. Table 8 presents the detailed results. For the CLAP score, the MusicGen model recorded a score of 0.5699, while the original AudioLDM achieved a score of 0.6497. The proposed method, depending on the LoRA configuration, achieved scores ranging from 0.5717 to 0.6995, with the best configuration (Rank = 8, Alpha = 16) showing a 7.7% improvement over the original AudioLDM. Overall, the proposed method consistently outperformed MusicGen, and showed variable performance compared with the original AudioLDM depending on the rank and alpha values. In terms of the KAD score, MusicGen scored 1.0022, while the original AudioLDM achieved a score of 1.1255, reflecting a high deviation from the reference audio. The proposed method significantly reduced the KAD score, achieving values between 0.2906 (Rank = 1, Alpha = 1) and 0.9936 (Rank = 8, Alpha = 8), depending on the configuration. The lowest score of 0.2906 represents a reduction of 74.2% compared with the original AudioLDM, while even the highest score achieved by the proposed method shows an improvement of 11.7%. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Prompt ID** & **Text Prompt** \\ \hline P1 & An energetic West African drumming rhythm using djembe and talking drum for a tribal celebration. \\ P2 & A fast-paced Japanese shamisen performance mixed with taiko drums in a festival atmosphere. \\ P3 & South American Andean folk tune with pan flute and charango, evoking highland travel. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Text prompts used for evaluating performance on non-Western music. Overall, the proposed method consistently outperformed MusicGen in terms of CLAP scores and showed varying performance compared with AudioLDM depending on the configuration.