# Enhancing MusicGen with Prompt Tuning
## 4 Experimental Results
### Evaluation Metrics  
Since music is inherently subjective and influenced by individual preferences, it is not easy to evaluate its quality using purely numerical metrics. However, by utilizing models that represent the characteristics of music as vectors, it becomes possible to quantitatively compare the similarity between music and texts or music and music. In the proposed method, we adopt two models: Contrastive Language-Audio Pretraining (CLAP) [27] to assess the alignment between text and music and Kullback-Leibler Divergence (KLD) [28] to represent the characteristics of music. In addition to semantic alignment, we observed that the audio quality of the generated music was preserved even for underrepresented or rare genres. Although subjective perception plays a role, the similarity in audio feature distributions quantified through low KLD values suggests that the proposed method maintains consistent audio characteristics across diverse musical styles. This indicates the model's capability not only to semantically align with textual prompts but also to generate sonically coherent outputs regardless of genre frequency in the training data. The performance of the proposed method was evaluated using the CLAP model [27], which quantifies the semantic alignment between text and audio. The CLAP model extends the idea of aligning text and visual representations to the domain of audio, enabling it to evaluate the semantic consistency between textual descriptions and audio signals. Specifically, the CLAP model was used to map both text and audio into a shared vector space, where their semantic similarity was assessed using cosine similarity. This metric provided a robust measure of the relevance of the generated music to the given text. Cosine similarity, ranging from \(-1\) to \(1\), was used to evaluate the semantic alignment between text prompts and generated music. Negative values indicated semantic opposition (e.g., generating piano sounds for a \"guitar sounds\" prompt), while values closer to \(1\) signified strong alignment between the prompt and the music. As the similarity score shifted from negative to positive, it reflected the model's progression toward generating music that better matched the intended semantic content. Despite its simplicity, cosine similarity effectively captured the degree of alignment and was thus adopted as a key metric in this proposed method. The KLD is a fundamental metric that measures the similarity between two probability distributions. Specifically, it quantifies how one probability distribution differs from another reference distribution by calculating their relative entropy. In information theory, the KLD is particularly useful for comparing the statistical characteristics of different distributions, where a lower value indicates greater similarity between them. In the proposed method, we utilized the KLD to evaluate the performance of our tuning method by comparing the audio characteristics of both the traditional and tuned models. We extracted four key audio features from the samples: Mel-frequency cepstral coefficients (MFCCs) [29] to capture timbal characteristics, chroma features [30] to represent pitch class distributions, spectral features [31] to analyze frequency spectrum characteristics, and the zero-crossing rate [32] to measure temporal properties. To establish a baseline for comparison, we first extracted these features from 336 evaluation dataset samples and computed their average distributions. We then used the KLD to calculate scores between this reference distribution and the features extracted from both the traditional model's outputs and our proposed model's outputs. This allowed us to quantitatively assess whether our tuning process improved the alignment of generated audio characteristics with those of the evaluation dataset, with lower scores indicating better alignment with the reference distribution.