AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work asks how few labelled real utterances are needed to make a frozen, synthetic-trained estimator useful, and suggests selecting synthetic checkpoints by linear correlation with true distances rather than by absolute error.
Experiments show that the proposed array-generic neural DoA estimation framework generalizes to previously unseen arrays, including mobile-phone-like configurations, without major performance degradation, while remaining competitive with conventional and learning-based baselines.
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