arXiv:2412.05694v1 Announce Type: new
Abstract: 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.
Music Visualizers: Blending Art and Technology
Music visualizers have long been used to enhance the auditory experience by adding a visual component to sound. This study presents a unique and innovative method for generating music visualizers using diffusion models, combining audio input with user-selected artwork. The multi-disciplinary nature of this concept lies in its integration of music analysis, art interpretation, and video generation techniques.
Image Generation and Artistic Style Descriptions
In the first stage of the process, music captioning and genre classification algorithms are employed to analyze the audio input. This analysis provides crucial information about the key musical features such as harmonics and percussives. Utilizing these features, artistic style descriptions are retrieved and combined with the user’s input image.
The diffusion model plays a central role in generating the images based on the user’s input and the artistic style descriptions. This technique allows for the creation of unique and visually stunning visuals that are in harmony with the music. The blending of audio and visual elements in this stage showcases the potential of this method to create immersive experiences.
Video Creation and Audio-Visual Synchrony
Once the images are generated, the same diffusion model is employed to interpolate frames and create a video. However, what sets this method apart is the use of audio energy vectors derived from the key musical features. These vectors control the interpolation, ensuring that the visual elements synchronize with the changes in audio energy.
The introduction of a new metric, Audio-Visual Synchrony (AVS), allows for a quantitative evaluation of the synchronisation between visual and audio elements. Comparative analysis has shown significantly higher AVS values for videos generated using the proposed method with audio energy vectors compared to linear interpolation. This indicates the effectiveness of this method in creating visually appealing and synchronized music visualizers.
Applications and Future Developments
The potential applications of this method are vast and span across various fields. Independent music video creators can use this technique to generate captivating visuals that complement their music. Film producers can incorporate this method in their productions to create unique and engaging visual experiences. Live music events can leverage this technology to enhance the audio-visual spectacle for the audience. Furthermore, this method can be applied in public spaces to create interactive and immersive audio-visual displays.
In relation to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, this study showcases the potential for integration of audio and visual elements in new and innovative ways. It highlights the important role that technology, such as diffusion models, can play in enhancing multimedia experiences. By bridging the gap between art and technology, this method paves the way for future developments in the field of music visualization and beyond.