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September 10, 2025Applied SciencesOpen Access

Enhancing Diffusion-Based Music Generation Performance with LoRA

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Authors

SKSung-Won KimGKGeonhui KimSYShoki Yagishita

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Overview

Novel low-rank adaptation method enhances text-to-music generation in AudioLDM, indicating improved genre-specific control.

Key Points

  • The proposed method significantly improves semantic alignment between text and generated music.
  • Contrastive language–audio pretraining scores increased by 0.0498, enhancing text-music consistency.
  • The kernel audio distance score decreased by 0.8349, indicating closer similarity to actual music distributions.
  • Mean opinion scores confirmed the perceptual quality of generated music, ranging from 3.5 to 3.8.

Cite This Study

Kim et al. (2025) studied this question.

synapsesocial.com/papers/68c1c63e54b1d3bfb60f251ehttps://doi.org/10.3390/app15158646
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Few-shot LoRA tuning for genre-specific music generation with semantic prompt matching2026
  2. 2MusicLDM: Enhancing Novelty in text-to-music Generation Using Beat-Synchronous mixup Strategies2024 · 71 citations
  3. 3Output Manipulation via LoRA for Generative AI2024 · 6 citations
  4. 4Multi-Track MusicLDM: Towards Versatile Music Generation with Latent Diffusion Model2024
  5. 5AudioLCM: Text-to-Audio Generation with Latent Consistency Models2024 · 1 citations