mlsanityData to 14 Oct 2026

SONIQUE: Video Background Music Generation Using Unpaired Audio-Visual Data

arXiv 2410.03879 ↗First posted 4 Oct 2024cs.SD cs.MM eess.AScited by 8

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Abstract (arXiv)

We present SONIQUE, a model for generating background music tailored to video content. Unlike traditional video-to-music generation approaches, which rely heavily on paired audio-visual datasets, SONIQUE leverages unpaired data, combining royalty-free music and independent video sources. By utilizing large language models (LLMs) for video understanding and converting visual descriptions into musical tags, alongside a U-Net-based conditional diffusion model, SONIQUE enables customizable music generation. Users can control specific aspects of the music, such as instruments, genres, tempo, and melodies, ensuring the generated output fits their creative vision. SONIQUE is open-source, with a demo available online.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Oct 2024.

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Citing papers per year: 2025: 3; 2026: 5.

8 papers.

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  1. 2024Diff-BGM: A Diffusion Model for Video Background Music Generation
  2. 2024MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization
  3. 2025Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation
  4. 2026Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation
  5. 2024Combining Genre Classification and Harmonic-Percussive Features with Diffusion Models for Music-Video Generation
  6. 2023Long-Term Rhythmic Video Soundtracker
  7. 2024Multi-Track MusicLDM: Towards Versatile Music Generation with Latent Diffusion Model
  8. 2024Quality-aware Masked Diffusion Transformer for Enhanced Music Generation