Research Paper#Audio Generation, Generative Models, GANs, Flow Matching🔬 ResearchAnalyzed: Jan 3, 2026 16:09
Flow2GAN: Hybrid Audio Generation for High Fidelity
Analysis
This paper introduces Flow2GAN, a novel framework for audio generation that combines the strengths of Flow Matching and GANs. It addresses the limitations of existing methods, such as slow convergence and computational overhead, by proposing a two-stage approach. The paper's significance lies in its potential to achieve high-fidelity audio generation with improved efficiency, as demonstrated by its experimental results and online demo.
Key Takeaways
- •Combines Flow Matching and GANs for efficient audio generation.
- •Addresses limitations of existing methods like slow convergence and computational overhead.
- •Introduces a two-stage framework with specific adaptations for audio.
- •Employs a multi-resolution network architecture.
- •Achieves better quality-efficiency trade-offs compared to existing methods.
Reference
“Flow2GAN delivers high-fidelity audio generation from Mel-spectrograms or discrete audio tokens, achieving better quality-efficiency trade-offs than existing state-of-the-art GAN-based and Flow Matching-based methods.”