Mask-Based Diffusion for Music Generation From Everyday Sounds
Year: 2025
Type: conference-paper
Abstract: The application of artificial intelligence in music generation has opened up new avenues for creative expression, transforming how we interpret and produce music. This paper presents a novel methodology for generating music from everyday sounds using generative AI. We use a spectrogram based approach combined with masking in the diffusion process and prompt driven style conditioning. A key contribution of our research is the introduction of masking into the Img2Img pipeline for selective music generation. We present a feature that allows for custom mask generation based on an input spectrogram and hyper-parameters. Using this, we achieve fine-grained creative control in music generation, resulting in polished outputs that maintain the essence of the input sound while seamlessly integrating prompt-driven characteristics that specify the music style. Through this approach, we demonstrate a new direction for spectrogram-based music generation that combines feature retention with flexible stylistic adjustments, enabling a versatile framework for creative audio synthesis.
Authors: Mahishi, Ananya, Senapati, Aayush, Halgeri, Shreya, Nagar, Aayush, Narayan, Surabhi
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077479285
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