# Text2FX: Harnessing CLAP Embeddings for Text-Guided Audio Effects

arXiv: https://arxiv.org/abs/2409.18847 · HTML: https://mlsanity.com/paper/2409.18847/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2024.

## Built on by (most cited first, 9 of 9)

- [SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering](https://mlsanity.com/paper/2508.03448/) (2025)
- [Do Joint Language-Audio Embeddings Encode Perceptual Timbre Semantics?](https://mlsanity.com/paper/2510.14249/) (2025)
- [One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization](https://mlsanity.com/paper/2601.09448/) (2026)
- [Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings](https://mlsanity.com/paper/2607.03806/) (2026)
- [Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior](https://mlsanity.com/paper/2505.11315/) (2025)
- [MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing](https://mlsanity.com/paper/2507.06329/) (2025)
- [Music Restoration via Latent Operator Optimization and Diffusion Model Priors](https://mlsanity.com/paper/2608.01972/) (2026)
- [Diff2Mix: Controllable Music Mixing via Diffusion Models and Differentiable Audio Effects](https://mlsanity.com/paper/2608.05442/) (2026)
- [Direct Preference Density Alignment for Conversational Audio Equalization](https://mlsanity.com/paper/2609.12607/) (2026)

## Abstract (arXiv)

This work introduces Text2FX, a method that leverages CLAP embeddings and differentiable digital signal processing to control audio effects, such as equalization and reverberation, using open-vocabulary natural language prompts (e.g., "make this sound in-your-face and bold"). Text2FX operates without retraining any models, relying instead on single-instance optimization within the existing embedding space, thus enabling a flexible, scalable approach to open-vocabulary sound transformations through interpretable and disentangled FX manipulation. We show that CLAP encodes valuable information for controlling audio effects and propose two optimization approaches using CLAP to map text to audio effect parameters. While we demonstrate with CLAP, this approach is applicable to any shared text-audio embedding space. Similarly, while we demonstrate with equalization and reverberation, any differentiable audio effect may be controlled. We conduct a listener study with diverse text prompts and source audio to evaluate the quality and alignment of these methods with human perception. Demos and code are available at anniejchu.github.io/text2fx.