# MorphFader: Enabling Fine-grained Controllable Morphing with Text-to-Audio Models

arXiv: https://arxiv.org/abs/2408.07260 · HTML: https://mlsanity.com/paper/2408.07260/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model](https://mlsanity.com/paper/2410.02144/) (2024)
- [Mix2Morph: Learning Sound Morphing from Noisy Mixes](https://mlsanity.com/paper/2601.20426/) (2026)
- [VoxMorph: Scalable Zero-shot Voice Identity Morphing via Disentangled Embeddings](https://mlsanity.com/paper/2601.20883/) (2026)
- [Beyond Descriptions: A Generative Scene2Audio Framework for Blind and Low-Vision Users to Experience Vista Landscapes](https://mlsanity.com/paper/2603.27295/) (2026)

## Abstract (arXiv)

Sound morphing is the process of gradually and smoothly transforming one sound into another to generate novel and perceptually hybrid sounds that simultaneously resemble both. Recently, diffusion-based text-to-audio models have produced high-quality sounds using text prompts. However, granularly controlling the semantics of the sound, which is necessary for morphing, can be challenging using text. In this paper, we propose textitMorphFader, a controllable method for morphing sounds generated by disparate prompts using text-to-audio models. By intercepting and interpolating the components of the cross-attention layers within the diffusion process, we can create smooth morphs between sounds generated by different text prompts. Using both objective metrics and perceptual listening tests, we demonstrate the ability of our method to granularly control the semantics in the sound and generate smooth morphs.