Title: The Use of Diffusion Models for Music Production
At the NeurIPS 2023 workshop on Diffusion Models, a paper was accepted that explores the potential of using diffusion models for conditional generation in music production. This innovative approach allows for the creation of music in 44.1kHz stereo audio with sampling-time guidance, opening up possibilities for a variety of realistic tasks.
Conditional generation from diffusion models can be applied to tasks such as continuation, inpainting, and regeneration of musical audio. It can also be used to create smooth transitions between different music tracks and transfer desired stylistic characteristics to existing audio clips.
The approach involves applying guidance at sampling time within a simple framework that supports reconstruction and classification losses, or any combination of the two. This ensures that the generated audio can match its surrounding context or conform to a specific class distribution or latent representation specified by a pre-trained classifier or embedding model.
The paper showcases randomly chosen samples for different creative applications, each conditioned on a given audio prompt. Table 1 displays the samples for each task and prompt, illustrating the potential of this approach in music production.
Task types include infill (replacing the middle two seconds of the prompt), regeneration (regenerating the middle two seconds of the prompt), continuation (generating a new continuation starting from the first 2.4 seconds of the prompt), transitions (regenerating a crossfaded section between two tracks), and guidance (generating a new clip conditioned on the PaSST classifier embedding of the prompt).
The prompts for these tasks are drawn from a test split of the Free Music Archive dataset, published under a Creative Commons Attribution 4.0 International License.
In conclusion, the use of diffusion models for conditional generation in music production holds immense potential for advancing creative applications and pushing the boundaries of audio production.