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Differentiable Time-Varying IIR Filtering for Real-Time Speech Denoising
🤖AI Summary
Researchers have developed TVF (Time-Varying Filtering), a lightweight 1 million parameter speech enhancement model that combines digital signal processing with deep learning for real-time speech denoising. The model uses a neural network to predict coefficients for a 35-band IIR filter cascade, offering interpretable processing while adapting dynamically to changing noise conditions.
Key Takeaways
- →TVF achieves real-time speech denoising with only 1 million parameters, making it highly efficient for deployment.
- →The model bridges traditional digital signal processing with modern deep learning for interpretable AI solutions.
- →Unlike black-box approaches, TVF provides explicit and adjustable spectral modifications through differentiable filtering.
- →The system dynamically adapts to non-stationary noise conditions using a 35-band IIR filter cascade.
- →Testing on the Valentini-Botinhao dataset shows effective performance compared to static DDSP and fully deep-learning approaches.
#speech-processing#deep-learning#signal-processing#real-time#audio-ai#neural-networks#denoising#interpretable-ai
Read Original →via arXiv – CS AI
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