One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917482562846720 |
|---|---|
| author | Stylianou, Ioannis Francombe, Jon Martinez-Nuevo, Pablo Shepstone, Sven Ewan Tan, Zheng-Hua |
| author_facet | Stylianou, Ioannis Francombe, Jon Martinez-Nuevo, Pablo Shepstone, Sven Ewan Tan, Zheng-Hua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09448 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization Stylianou, Ioannis Francombe, Jon Martinez-Nuevo, Pablo Shepstone, Sven Ewan Tan, Zheng-Hua Sound Artificial Intelligence 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. |
| title | One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2601.09448 |