A text-conditioned diffusion model that generates audio-latent queries in a frozen audio embedding space improves in-domain text-music retrieval and enables post-hoc controllability via negative prompting and DDIM inversion.
Using a pretrained latent space op- timized for audio-audio retrieval, we train a generative dif- fusion model conditioned on text to generate audio latent embeddings in this space
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GD-Retriever: Controllable Generative Text-Music Retrieval with Diffusion Models
A text-conditioned diffusion model that generates audio-latent queries in a frozen audio embedding space improves in-domain text-music retrieval and enables post-hoc controllability via negative prompting and DDIM inversion.