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20232026
most citedFoundation Models for Music: A Survey

8 citations · 17 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.SD2025

Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching

Ben Hayes, Charalampos Saitis, György Fazekas

Many audio synthesizers can produce the same signal given different parameter configurations, meaning the inversion from sound to parameters is an inherently ill-posed problem. We…

cs.SD2025★ 1 cited

Mamba-Diffusion Model with Learnable Wavelet for Controllable Symbolic Music Generation

Jincheng Zhang, György Fazekas, Charalampos Saitis

The recent surge in the popularity of diffusion models for image synthesis has attracted new attention to their potential for generation tasks in other domains. However, their appl…

cs.SD2024★ 1 cited

Exploring Transformer-Based Music Overpainting for Jazz Piano Variations

Eleanor Row, Ivan Shanin, György Fazekas

This paper explores transformer-based models for music overpainting, focusing on jazz piano variations. Music overpainting generates new variations while preserving the melodic and…

cs.SD2024★ 8 cited

Foundation Models for Music: A Survey

Yinghao Ma, Anders Øland, Anton Ragni +39

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…

cs.SD2023★ 5 cited

Fast Diffusion-GAN Model for Generating Symbolic Music with Emotions

Jincheng Zhang, György Fazekas, Charalampos Saitis

Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. However, little progress has been made o…

cs.SD2023★ 1 cited

Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models

Jincheng Zhang, György Fazekas, Charalampos Saitis

Emerging Denoising Diffusion Probabilistic Models (DDPM) have become increasingly utilised because of promising results they have achieved in diverse generative tasks with continuo…