# Enhancing MusicGen with Prompt Tuning
## 5 Discussion  
The results obtained from the experiments revealed that the proposed method effectively addresses key limitations of traditional approaches, particularly the narrow range of genres and styles and the misunderstanding of text prompts. By employing prompt tuning, the proposed method consistently improves the alignment between text prompts and generated music, enabling more accurate and reliable outputs. These improvements are evident across diverse test cases, with notable gains in both semantic alignment and structural coherence. The results highlight the adaptability and robustness of the proposed method, establishing its potential as a scalable solution for enhancing generative music models across a wide range of applications. The proposed method contributes to society by enhancing the accessibility and diversity of music creation. By enabling generative models to produce high-quality outputs for under-represented genres, such as traditional Korean music, the method supports the preservation and appreciation of cultural heritage. Furthermore, its improved alignment between text prompts and generated music lowers barriers for users, allowing a broader audience to explore and engage with music creation, regardless of their level of expertise. This demonstrates the potential of AI technologies to complement human creativity and support cultural expression in meaningful ways. Reflecting on the challenges identified in the introduction, the limitations of traditional approaches in handling diverse musical styles and user requirements are addressed through the integration of prompt tuning into MusicGen. By building upon prior research in text-to-music generation and PEFT methods, the proposed method demonstrates its ability to overcome biases in training data and inefficiencies in fine-tuning. This study highlights how prompt tuning, as a lightweight yet powerful technique, can adapt generative models to specific genres, such as traditional Korean music, while maintaining scalability and efficiency. Together, these contributions underscore the potential of combining foundational research with innovative methodologies to advance generative AI in creative and culturally significant directions. To assess the broader applicability of these benefits, we next examine how the same prompt-tuning principle could be extended beyond MusicGen to other text-to-music architectures. Although our experiments focus on MusicGen, the same soft prompt-tuning strategy can, in principle, be ported to symbolic-token transformers (e.g., Mustango [6]) or diffusion-based systems such as AudioLDM [5]. However, two practical challenges arise. First, different models operate in distinct representation spaces--symbolic MIDI tokens, spectrogram latents, or audio codec--so the prompt embedding must be inserted at architecture-specific positions (for example, the text encoder of a diffusion U-Net) and resized to match layer widths. Second, some frameworks do not freeze the text encoder by default, meaning that isolating a small set of trainable parameters may require additional gating or adapter layers. Addressing these issues could involve minor architectural modifications or combining prompt tuning with low-rank adapters; a systematic investigation of such variants is left to future work. A further limitation concerns the audio representation itself. The Encodec quantizer embedded in MusicGen was pre-trained primarily on Western-instrument datasets such as Jamendo, whereas traditional Korean instruments (e.g., gayageum, haegeum, and taepyeongso) display spectral envelopes characterized by strong odd harmonics, micro-tonal vibrato, and high-\(Q\) resonances that are scarcely represented in that corpus. A preliminary log-spectral-distance probe on a small set of solo clips indicates that Encodec reconstructs these timbres with slightly higher distortion than comparable Western strings, hinting at a codec mismatch.