SoundLoCD: An Efficient Conditional Discrete Contrastive Latent Diffusion Model for Text-to-Sound Generation
<|reference_start|>SoundLoCD: An Efficient Conditional Discrete Contrastive Latent Diffusion Model for Text-to-Sound Generation: We present SoundLoCD, a novel text-to-sound generation framework, which incorporates a LoRA-based conditional discrete contrastive latent diffusion model. Unlike recent large-scale sound generation models, our model can be efficiently trained under limited computational resources. The integration of a contrastive learning strategy further enhances the connection between text conditions and the generated outputs, resulting in coherent and high-fidelity performance. Our experiments demonstrate that SoundLoCD outperforms the baseline with greatly reduced computational resources. A comprehensive ablation study further validates the contribution of each component within SoundLoCD. Demo page: \url{https://XinleiNIU.github.io/demo-SoundLoCD/}.<|reference_end|>
@article{niu2024soundlocd:,
    title={SoundLoCD: An Efficient Conditional Discrete Contrastive Latent
  Diffusion Model for Text-to-Sound Generation},
    author={Xinlei Niu, Jing Zhang, Christian Walder, Charles Patrick Martin},
    journal={arXiv preprint arXiv:2405.15338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15338},
    primaryClass={cs.SD eess.AS}
}