arXiv:2601.09448v3 Announce Type: replace-cross Abstract: 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
The proliferation of Large Language Models has enabled new forms of human-computer interaction, making it feasible to apply LLMs to complex, real-time audio equalization tasks.
This development represents a significant step towards more intuitive and personalized control over audio environments, moving beyond static settings to adaptive, AI-driven solutions.
Audio equalization, traditionally a manual and technical process, can now be dynamically adjusted via natural language, making sophisticated sound control accessible to a broader audience.
- · Audio technology companies
- · Consumers of audio devices
- · AI developers
- · Music and entertainment sector
- · Manufacturers of traditional audio equalizers
- · Manual audio engineers (for routine tasks)
Audio systems will become more personalized and context-aware, adapting to individual preferences and environmental conditions.
This conversational interface could extend to other complex device controls, accelerating the integration of LLMs into consumer electronics.
The ability to dynamically shape soundscapes via AI may lead to new forms of immersive audio experiences and therapeutic sound applications.
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Read at arXiv cs.AI