Programmatic Pitch

Diffusion-based Music Synthesis

Abstract

Diffusion models have firmly established their excellence for image generation tasks, as evidenced by the success of renowned models such as DALL-E, Midjourney, and Stable Diffusion. This advanced development of diffusion models for visual content raises an interesting question: can diffusion models be adapted for audio generation tasks? In this study, we introduce a novel diffusion model architecture designed to generate mel spectrograms, visual representations of sound which can subsequently be converted into audible music. Given the exceptional capability of diffusion models to produce high-

quality images, their application to mel spectrogram generation is particularly promising. Our proposed diffusion model deviates minimally from the conventional architectures employed for visual content, making this research especially useful for examining the potential for cross-domain application between image and audio generation. The proposed model has been trained on a dataset consisting of over 186 hours of Lofi audio, offering the model diverse samples for generalized learning. However, a combination of research limitations led to subpar results, paving the way for further studies to build on this one.