# Enhancing MusicGen with Prompt Tuning
## 1 Introduction  
Large-scale music generation models [1, 2, 3] can convert text or audio inputs into musical compositions, expanding music creation possibilities. These models provide a streamlined way to explore and produce music, making the process more accessible to a wide range of users. Some music generation models generate audio output based on text or audio inputs, offering new possibilities for music generation. Specifically, text-to-music models, which translate textual descriptions into musical compositions, have gained significant attention for their ability to bridge natural language and music, but they face several key challenges. First, text-to-music models [1, 2, 3, 4, 5, 6, 7] perform well for genres that are well represented in their training data, but they fail to adequately represent rare genres or complex styles. The limitation stems from the biases caused by the limited generalizability of both the training data and the models themselves, making it challenging to adapt to new styles or genres [4, 8]. Second, such models contain structural complexity. While various efforts have been made to improve them, text-to-music models face more challenges than text-to-text models due to the structural limitations in representing musical expressions. To address this,approaches [5] have been proposed to effectively learn musical representations or reduce the gap between text and music representations. Third, there is a lack of evaluation frameworks, which makes it difficult to reliably assess music generation models' performance. Typically, they are evaluated subjectively or through experiments conducted on limited datasets. Although automated music evaluation approaches [9; 10; 11; 12] have been proposed, eliminating subjective factors in music evaluation is still a challenge. In particular, there are the following problems related to the issue of applying text-to-music models to specific genres and styles. First, the efforts to apply music generation models to diverse styles have primarily relied on traditional approaches like data augmentation [7; 13; 14] and style transfer [15; 16]. The former enhances diversity by modifying existing data, while the latter combines source and target styles to create new ones. However, ensuring the precision of complex style transfer remains a challenge. The traditional approach, MusicVAE [17], leveraged a hierarchical structure in latent space to address these issues, but the lack of dataset diversity and the limited precision of style transfer techniques have not been resolved. Second, there is a lack of precision in the reflection of user requirements. The text-to-music models aim to generate music based on text prompts, leveraging advanced techniques to bridge the gap between textual descriptions and musical outputs. Among these, MusicGen [1] stands out as a model that uses text prompts as conditional inputs, utilizing compressed multi-stream representations to learn the semantic relationships between text and music and employing a transformer-based decoder. However, even this model struggles to fully incorporate detailed user requirements, such as specific tempos, instrument combinations, or complex styles. When user prompts are ambiguous or include multiple conditions, the generated outputs often reflect only partial requirements or fail to meet expectations [2; 3]. Third, the large-scale generative models require significant computational resources for both training and inference. The additional training of pretrained models like MusicGen requires considerable computational power and costs. Transfer learning leverages the knowledge of traditional models to adapt to new tasks and reduce training costs, but it suffers from performance degradation when there is a domain gap. To address these challenges, research focusing on efficient training techniques such as parameter-efficient fine-tuning (PEFT) [18; 19] is necessary.