# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 4 Experiments
### Results
In contrast, for the KAD scores, the proposed method consistently achieved better results than both MusicGen and AudioLDM in all settings. These results demonstrate that the proposed method can achieve better semantic alignment and audio consistency compared with both MusicGen and the original AudioLDM. A subjective evaluation was conducted using the MOS to assess the perceptual quality of the generated audio. A total of 45 participants, recruited from the general public, rated the generated audio sample via Google Form on a scale from 1 (very poor) to 5 (excellent), based on three criteria: naturalness of the music, overall sound quality and alignment with the input text prompt. Table 9 presents the detailed results of this evaluation. Table 10 summarizes the CLAP and KAD scores for the baseline and our proposed method across three non-Western prompts. Our method consistently outperformed the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Alpha** & **200 Epoch** & **400 Epoch** & **600 Epoch** & **800 Epoch** & **1000 Epoch** \\ \hline
1 & 1 & 0.6269 & 0.5717 & 0.5985 & 0.5858 & 0.5870 \\
1 & 2 & 0.6411 & 0.6212 & 0.6556 & 0.6411 & 0.6371 \\
2 & 2 & 0.6572 & 0.6221 & 0.6225 & 0.6412 & 0.6454 \\
2 & 4 & 0.6299 & 0.6475 & 0.6412 & 0.6594 & 0.6385 \\
4 & 4 & 0.6181 & 0.6386 & 0.6141 & 0.6449 & 0.6314 \\
4 & 8 & 0.6298 & 0.6499 & 0.6218 & 0.6600 & 0.6193 \\
8 & 8 & 0.6610 & 0.6399 & 0.6461 & 0.6583 & 0.6784 \\
8 & 16 & 0.6941 & 0.6451 & 0.5813 & 0.6995 & 0.6644 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CLAP scores of the proposed method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Alpha** & **200 Epoch** & **400 Epoch** & **600 Epoch** & **800 Epoch** & **1000 Epoch** \\ \hline
1 & 1 & 0.2906 & 0.4394 & 0.4430 & 0.4695 & 0.5088 \\
1 & 2 & 0.4490 & 0.5313 & 0.4969 & 0.4488 & 0.4248 \\
2 & 2 & 0.3305 & 0.4241 & 0.4860 & 0.4029 & 0.3431 \\
2 & 4 & 0.5183 & 0.5340 & 0.5305 & 0.5116 & 0.4682 \\
4 & 4 & 0.7018 & 0.6690 & 0.6611 & 0.8369 & 0.7420 \\
4 & 8 & 0.6018 & 0.7340 & 0.6610 & 0.7665 & 0.8972 \\
8 & 8 & 0.7851 & 0.9936 & 0.7851 & 0.7540 & 0.6702 \\
8 & 16 & 0.8566 & 0.7362 & 0.6140 & 0.8269 & 0.8476 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: KAD scores of the proposed method. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Config** & **CLAP Score \(\uparrow\)** & **KAD Score \(\downarrow\)** \\ \hline Fine-tuned MusicGen & 0.5699 & 1.0022 \\ Baseline model & 0.6497 & 1.1255 \\ Rank = 1, Alpha = 1 (400 epoch) & 0.5717 & 0.4394 \\ Rank = 1, Alpha = 1 (200 epoch) & 0.6269 & 0.2906 \\ Rank = 8, Alpha = 8 (400 epoch) & 0.6399 & 0.9936 \\ Rank = 8, Alpha = 16 (800 epoch) & 0.6995 & 0.8269 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of scores for fine-tuned MusicGen, baseline model, and the proposed method. baseline, with higher CLAP scores and lower KAD scores, indicating improved semantic alignment and distributional similarity to real music. In addition, we compared the generation latency to evaluate the computational overhead introduced by our approach. As shown in Table 11, the average generation time increased from 6.72 s to 7.64 s, with the real-time factor rising from 1.68\(\times\) to 1.91\(\times\). Although this reflected a modest computational cost, the gain in controllability and musical quality justified the trade-off.