DiffWave is a non-autoregressive diffusion model that generates high-fidelity audio waveforms from noise in constant steps, matching WaveNet vocoder quality while being orders of magnitude faster and outperforming prior models in unconditional generation.
Ddsp: Differentiable digital signal processing
4 Pith papers cite this work. Polarity classification is still indexing.
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DART adds differentiability to acoustic radiance transfer, enabling material optimization and improved performance on sparse acoustic field prediction tasks compared to signal processing and neural baselines.
Explicit noise modeling during differentiable optimization improves attenuation filter accuracy in feedback delay networks when room impulse responses are noisy.
citing papers explorer
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DiffWave: A Versatile Diffusion Model for Audio Synthesis
DiffWave is a non-autoregressive diffusion model that generates high-fidelity audio waveforms from noise in constant steps, matching WaveNet vocoder quality while being orders of magnitude faster and outperforming prior models in unconditional generation.
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Differentiable Acoustic Radiance Transfer
DART adds differentiability to acoustic radiance transfer, enabling material optimization and improved performance on sparse acoustic field prediction tasks compared to signal processing and neural baselines.
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Learning Filters in Feedback Delay Networks from Noisy Room Impulse Responses
Explicit noise modeling during differentiable optimization improves attenuation filter accuracy in feedback delay networks when room impulse responses are noisy.
- Go witheFlow: Real-time Emotion Driven Audio Effects Modulation