A Novel Diffusion Model Based Approach for Sleep Therapeutic Music Generation

Timo Hromadka, Kevin Monteiro, Sam Nallaperuma
Proceedings of Machine Learning Research, PMLR 303:1-15, 2026.

Abstract

Sleep disorders, particularly insomnia, and mental health conditions affect a significant fraction of adults worldwide, posing serious mental and physical health risk. Music therapy offers promising, low-cost, and non-invasive treatment, but current approaches rely heavily on expert-curated playlists, limiting scalability and personalization. We propose a low-cost generative system leveraging recent advances in diffusion models to synthesize music for therapy. We focus on insomnia and curate a dataset of waveform sleep music to generate audio tailored to sleep. To ensure real-world feasibility, we optimize our system for training and use on a single GPU, balancing quality and efficiency through extensive ablation studies. We show through subjective human evaluations that our generated music matches or outperforms existing baselines in both perceived quality and relevance to sleep therapy, while using only a fraction of the computational cost.

Cite this Paper

BibTeX
@InProceedings{pmlr-v303-hromadka26a, title = {A Novel Diffusion Model Based Approach for Sleep Therapeutic Music Generation}, author = {Hromadka, Timo and Monteiro, Kevin and Nallaperuma, Sam}, booktitle = {Proceedings of Machine Learning Research}, pages = {1--15}, year = {2026}, editor = {Herremans, Dorien and Bhandari, Keshav and Roy, Abhinaba and Colton, Simon and Barthet, Mathieu}, volume = {303}, series = {Proceedings of Machine Learning Research}, month = {26 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v303/main/assets/hromadka26a/hromadka26a.pdf}, url = {https://proceedings.mlr.press/v303/hromadka26a.html}, abstract = {Sleep disorders, particularly insomnia, and mental health conditions affect a significant fraction of adults worldwide, posing serious mental and physical health risk. Music therapy offers promising, low-cost, and non-invasive treatment, but current approaches rely heavily on expert-curated playlists, limiting scalability and personalization. We propose a low-cost generative system leveraging recent advances in diffusion models to synthesize music for therapy. We focus on insomnia and curate a dataset of waveform sleep music to generate audio tailored to sleep. To ensure real-world feasibility, we optimize our system for training and use on a single GPU, balancing quality and efficiency through extensive ablation studies. We show through subjective human evaluations that our generated music matches or outperforms existing baselines in both perceived quality and relevance to sleep therapy, while using only a fraction of the computational cost.} }
Endnote
%0 Conference Paper %T A Novel Diffusion Model Based Approach for Sleep Therapeutic Music Generation %A Timo Hromadka %A Kevin Monteiro %A Sam Nallaperuma %B Proceedings of Machine Learning Research %C Proceedings of Machine Learning Research %D 2026 %E Dorien Herremans %E Keshav Bhandari %E Abhinaba Roy %E Simon Colton %E Mathieu Barthet %F pmlr-v303-hromadka26a %I PMLR %P 1--15 %U https://proceedings.mlr.press/v303/hromadka26a.html %V 303 %X Sleep disorders, particularly insomnia, and mental health conditions affect a significant fraction of adults worldwide, posing serious mental and physical health risk. Music therapy offers promising, low-cost, and non-invasive treatment, but current approaches rely heavily on expert-curated playlists, limiting scalability and personalization. We propose a low-cost generative system leveraging recent advances in diffusion models to synthesize music for therapy. We focus on insomnia and curate a dataset of waveform sleep music to generate audio tailored to sleep. To ensure real-world feasibility, we optimize our system for training and use on a single GPU, balancing quality and efficiency through extensive ablation studies. We show through subjective human evaluations that our generated music matches or outperforms existing baselines in both perceived quality and relevance to sleep therapy, while using only a fraction of the computational cost.
APA
Hromadka, T., Monteiro, K. & Nallaperuma, S.. (2026). A Novel Diffusion Model Based Approach for Sleep Therapeutic Music Generation. Proceedings of Machine Learning Research, in Proceedings of Machine Learning Research 303:1-15 Available from https://proceedings.mlr.press/v303/hromadka26a.html.

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