Long-Form Text-to-Music Generation for TRPG Soundtracks Using Adaptive Prompts and LLMs: A Comparative Study

Core Concepts

Using detailed music descriptions generated from in-game dialogue by LLMs improves the audio quality of long-form music generated by text-to-music models, while maintaining consistency in these descriptions over time leads to smoother transitions between musical segments.

Abstract

• Bibliographic Information: Marra, F., & Ferreira, L. N. (2024). Long-Form Text-to-Music Generation with Adaptive Prompts: A Case of Study in Tabletop Role-Playing Games Soundtracks. In Proceedings of the 1st Latin American Music Information Retrieval Workshop. Rio de Janeiro, Brazil.
• Research Objective: This paper investigates the capabilities of text-to-audio music generation models in producing long-form music with prompts that change over time, focusing on soundtrack generation for Tabletop Role-Playing Games (TRPGs).
• Methodology: The researchers developed Babel Bardo, a system that uses Large Language Models (LLMs) to transform speech transcriptions of TRPG gameplay into music descriptions. These descriptions are then fed to a text-to-music model to generate 30-second music pieces. Four versions of Babel Bardo were compared: a baseline using direct speech transcriptions, an emotion-based version, and two versions with varying levels of description detail and continuation. The models were evaluated on two TRPG campaigns in English and Portuguese, using Fréchet Audio Distance (FAD) for audio quality, Kullback-Leibler Divergence (KLD) for story alignment, and transition smoothness between generated segments.
• Key Findings: Detailed music descriptions generated by LLMs improved audio quality, while maintaining consistency across consecutive descriptions enhanced story alignment and transition smoothness. The study also found that emotion serves as an effective signal for aligning generated music with TRPG narratives.
• Main Conclusions: The research demonstrates the potential of using LLMs and text-to-music models for generating dynamic, long-form soundtracks for TRPGs. The authors suggest that future work should focus on balancing audio quality with story alignment and transition smoothness.
• Significance: This research contributes to the field of text-to-music generation by exploring the challenges of long-form music generation with adaptive prompts. It also highlights the potential of this technology for enhancing the immersive experience of TRPGs.
• Limitations and Future Research: The study is limited by the use of only two TRPG campaigns and a single text-to-music model. Future research could explore the impact of different LLMs, text-to-music models, and TRPG genres on the generated music. User studies could also provide valuable insights into the subjective quality and immersiveness of the generated soundtracks.

Stats

MusicGen, a text-to-music model with 1.3B parameters, was used for generating 30-second audio segments.
Ollama 3.1, a 70B parameter LLM, was used for generating music descriptions.
Fréchet Audio Distance (FAD) scores indicated that Babel Bardo-DC, the version with detailed descriptions and continuation, produced the highest audio quality.
Kullback-Leibler Divergence (KLD) scores showed that Babel Bardo-Emotion, using emotion as the primary prompt, achieved the best story alignment.
Babel Bardo-Emotion also exhibited the smoothest transitions between music segments, as measured by KLD.

Quotes

"Results suggest that while detailed music descriptions contribute to improved audio quality, maintaining consistency across consecutive descriptions helps achieve smoother transitions between musical segments."
"Furthermore, our findings indicate that emotion serves as an effective signal for aligning generated music with TRPG narratives."

Key Insights Distilled From

Long-Form Text-to-Music Generation with Adaptive Prompts: A Case of Study in Tabletop Role-Playing Games Soundtracks

by Felipe Marra... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03948.pdf

Deeper Inquiries

How can the balance between audio quality, story alignment, and transition smoothness be further optimized in long-form music generation for dynamic media like TRPGs?

Balancing audio quality, story alignment, and transition smoothness in long-form music generation for dynamic media like TRPGs presents a complex optimization problem. Here are potential avenues for improvement:
1. Enhanced Contextual Awareness:

Longer Time Scales: Instead of 30-second segments, explore using larger chunks of the TRPG narrative (e.g., minutes) as input for the LLM. This could provide a broader context for music generation, potentially leading to better story alignment and smoother transitions.
Multimodal Input: Go beyond just speech transcriptions. Incorporate additional game state information like character actions, locations, and game master descriptions. This richer context could enable the system to generate more relevant and immersive music.
2. Advanced Music Description Generation:

Fine-tuning LLMs: Train LLMs specifically on datasets of TRPG transcripts paired with human-annotated music descriptions. This could improve the LLM's ability to generate descriptions that are both musically rich and narratively aligned.
Hierarchical Descriptions: Explore generating music descriptions at multiple levels of granularity. For example, a high-level description could set the overall mood of a scene, while lower-level descriptions could control specific musical elements (instrumentation, tempo) to reflect moment-to-moment changes.
3. Refined Transition Techniques:

Music Generation Models with Explicit Transition Control: Investigate using or developing text-to-music models that allow for more explicit control over transitions. This could involve techniques like crossfading, thematic bridging, or dynamic manipulation of musical parameters.
Reinforcement Learning: Train reinforcement learning agents to evaluate and optimize transitions based on metrics like smoothness, coherence, and story alignment.
4. Human-in-the-Loop Systems:

Interactive Music Generation: Allow game masters or players to provide feedback on the generated music in real-time, enabling the system to adapt and refine its output based on human preferences.
Hybrid Approaches: Combine AI-generated music with curated music libraries, allowing for a blend of original and pre-existing tracks that can be dynamically selected and mixed to match the evolving narrative.
By pursuing these research directions, we can strive for more sophisticated AI systems that enhance the immersive storytelling potential of TRPGs and other interactive media experiences.

Could the reliance on pre-defined emotions limit the system's ability to capture the nuances and complexities of human storytelling in TRPGs?

Yes, relying solely on a limited set of pre-defined emotions like "Happy," "Calm," "Agitated," or "Suspenseful" can significantly restrict the system's capacity to grasp the subtle nuances and multifaceted nature of human storytelling in TRPGs.
Here's why:

Emotional Spectrum: Human emotions exist on a spectrum, far more intricate than a few basic categories. TRPGs often evoke blended emotions, such as bittersweet joy, nervous anticipation, or melancholic reflection, which pre-defined labels might not adequately represent.
Contextual Interpretation: The same emotion can be expressed and perceived differently depending on the context. A character's "anger" during a tense negotiation might require different musical accompaniment than "anger" displayed in a climactic battle.
Narrative Depth: TRPG stories often delve into complex themes, character development, and moral dilemmas. Reducing these narratives to simple emotional cues could flatten the emotional impact and limit the music's ability to enhance the storytelling.
To address this limitation, consider these alternatives:

Emotion Embeddings: Instead of discrete labels, use emotion embeddings derived from large language models. These embeddings capture a wider range of emotions and their nuances.
Sentiment Analysis: Employ sentiment analysis techniques that go beyond basic emotion classification to identify more nuanced emotional tones, such as sarcasm, irony, or hopefulness.
Affective Musical Attributes: Instead of directly mapping emotions to music, focus on generating music based on affective musical attributes like energy, valence (positivity/negativity), and tension. This allows for a more flexible and nuanced emotional expression in the music.
By moving away from rigid emotion categories and embracing more sophisticated approaches, AI systems can better capture the richness and complexity of human emotions in TRPG storytelling.

What are the broader implications of using AI to generate real-time, adaptive soundtracks for other forms of interactive entertainment beyond TRPGs?

The use of AI to generate real-time, adaptive soundtracks holds transformative potential for various interactive entertainment forms beyond TRPGs, revolutionizing how we experience and engage with these media:
1. Video Games:

Dynamic Soundscapes: Imagine video games where the music seamlessly adapts to the player's actions, choices, and the in-game environment, creating a truly immersive and emotionally resonant experience.
Personalized Gameplay: AI-generated soundtracks could tailor the music to individual player profiles, enhancing emotional engagement and potentially influencing player behavior and game progression.
2. Virtual Reality (VR) and Augmented Reality (AR):

Heightened Presence and Immersion: In VR/AR experiences, adaptive soundtracks can deepen the sense of presence by responding to the user's movements, interactions, and the virtual environment, blurring the lines between the real and the digital.
Interactive Storytelling: AI-generated music could dynamically shape the narrative in VR/AR stories, creating a more personalized and engaging experience for each user.
3. Live Performances and Interactive Art:

Real-time Collaboration: Musicians and artists could collaborate with AI systems to create dynamic and improvisational performances where the music responds to their input and the audience's reactions.
Evolving Art Experiences: AI-generated soundtracks could transform art installations and interactive exhibits, creating unique and personalized soundscapes that react to visitor presence and engagement.
4. Accessibility and Inclusivity:

Adaptive Audio for Diverse Needs: AI can generate personalized soundtracks for individuals with visual or auditory impairments, enhancing accessibility and creating more inclusive entertainment experiences.
Ethical Considerations:

Bias and Representation: As with any AI system, it's crucial to address potential biases in training data to ensure that generated soundtracks are culturally sensitive and inclusive.
Intellectual Property: Clear guidelines regarding ownership and copyright of AI-generated music will be essential as this technology becomes more prevalent.
The development of AI-powered, real-time adaptive soundtracks marks a significant step towards more immersive, personalized, and engaging entertainment experiences across a wide range of interactive media. As the technology matures, it promises to redefine how we create, consume, and connect with digital worlds.