An abstract sound is defined as a sound that does not disclose identifiable real-world sound events to a listener. Sound fusion aims to synthesize an original sound and a reference sound to generate a novel sound that exhibits auditory features beyond mere additive superposition of the sound constituents. To achieve this fusion, we employ inversion techniques that preserve essential features of the original sample while enabling controllable synthesis. We propose novel SDE and ODE inversion models based on DPMSolver++ samplers that reverse the sampling process by configuring model outputs as constants, eliminating circular dependencies incurred by noise prediction terms. Our inversion approach requires no prompt conditioning while maintaining flexible guidance during sampling.
View on arXiv@article{liu2025_2506.11811,
title={ Abstract Sound Fusion with Unconditioned Inversion Model },
author={ Jing Liu and EnQi Lian },
journal={arXiv preprint arXiv:2506.11811},
year={ 2025 }
}