AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The main contribution is an interpretability-informed curriculum design for blind acoustic parameter estimation with clearer scope and improved reproducibility for ViT-based models.
This work frames this as Domain-Incremental Learning over acoustic environments and presents the first continual learning benchmark for multi-channel replay speech detection, evaluating a state-of-the-art beamformer-based detector over all 24 environment orderings of the ReMASC corpus with five seeds.
Speech enhancement (SE) models degrade under domain shifts and have to adapt to unseen target domains during deployment. Most existing test-time adaptation (TTA) methods for SE do so by adapting a subset of the model weights using a self-supervised loss, which requires backpropagation at test-time and permanently alter...
Increasing the overall realism of synthetic acoustic training data improves the generalization of DeepFilterNet3 to unseen measured environments and shows that increasing the overall realism of synthetic acoustic training data improves the generalization of DeepFilterNet3 to unseen measured environments.
The transition from vocoder-based to neural-codec speech synthesis makes generalization more difficult for speech deepfake detectors, particularly those relying on speech-oriented representations. It remains unclear which acoustic representations retain discriminative information when the generation mechanism changes....
PRISM (Prototype-Rectified Iterative Self-supervised Manifold Denoising), a training-free, source-free TTA framework grounded in the Affine Noise Hypothesis, is addressed, making it substantially faster than gradient-based TTA while requiring no additional training.
Analysis of the results indicates that this multi-objective optimization framework achieves superior speech enhancement by simultaneously addressing magnitude and phase reconstruction, waveform-level distortion, and psychoacoustic naturalness, a balance not achieved in any prior works so far.
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