# Combining Genre Classification and Harmonic-Percussive Features with Diffusion Models for Music-Video Generation

arXiv: https://arxiv.org/abs/2412.05694 · HTML: https://mlsanity.com/paper/2412.05694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 7 Dec 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023)
- [MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation](https://mlsanity.com/paper/2209.09002/) (2022)
- [Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion](https://mlsanity.com/paper/2310.03502/) (2023)
- [LP-MusicCaps: LLM-Based Pseudo Music Captioning](https://mlsanity.com/paper/2307.16372/) (2023)
- [Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16223/) (2023)
- [CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP](https://mlsanity.com/paper/2203.00386/) (2022)
- [musicnn: Pre-trained convolutional neural networks for music audio tagging](https://mlsanity.com/paper/1909.06654/) (2019)
- [MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response](https://mlsanity.com/paper/2309.08730/) (2023)
- [MusCaps: Generating Captions for Music Audio](https://mlsanity.com/paper/2104.11984/) (2021)
- [AADiff: Audio-Aligned Video Synthesis with Text-to-Image Diffusion](https://mlsanity.com/paper/2305.04001/) (2023)
- [Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts](https://mlsanity.com/paper/2009.08083/) (2020)

## Abstract (arXiv)

This study presents a novel method for generating music visualisers using diffusion models, combining audio input with user-selected artwork. The process involves two main stages: image generation and video creation. First, music captioning and genre classification are performed, followed by the retrieval of artistic style descriptions. A diffusion model then generates images based on the user's input image and the derived artistic style descriptions. The video generation stage utilises the same diffusion model to interpolate frames, controlled by audio energy vectors derived from key musical features of harmonics and percussives. The method demonstrates promising results across various genres, and a new metric, Audio-Visual Synchrony (AVS), is introduced to quantitatively evaluate the synchronisation between visual and audio elements. Comparative analysis shows significantly higher AVS values for videos generated using the proposed method with audio energy vectors, compared to linear interpolation. This approach has potential applications in diverse fields, including independent music video creation, film production, live music events, and enhancing audio-visual experiences in public spaces.