Population-Aligned Audio Reproduction With LLM-Based Equalizers
This work addresses the need for more accessible and context-aware audio tuning methods for users, though it is incremental as it applies existing LLM techniques to a new domain.
The paper tackled the problem of static, manual audio equalization by introducing an LLM-based system that maps natural language prompts to equalization settings, achieving statistically significant improvements in aligning with population preferences over baselines.
Conventional audio equalization is a static process that requires manual and cumbersome adjustments to adapt to changing listening contexts (e.g., mood, location, or social setting). In this paper, we introduce a Large Language Model (LLM)-based alternative that maps natural language text prompts to equalization settings. This enables a conversational approach to sound system control. By utilizing data collected from a controlled listening experiment, our models exploit in-context learning and parameter-efficient fine-tuning techniques to reliably align with population-preferred equalization settings. Our evaluation methods, which leverage distributional metrics that capture users' varied preferences, show statistically significant improvements in distributional alignment over random sampling and static preset baselines. These results indicate that LLMs could function as "artificial equalizers," contributing to the development of more accessible, context-aware, and expert-level audio tuning methods.