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Generalized Operating Procedure for Deep Learning: an Unconstrained Optimal Design Perspective
In this paper, we intend to address this problem by presenting a generalized operating procedure for DL from the perspective of unconstrained optimal design, which is motivated by a simple intension to remove the barrier of using DL, especially for those scientists or engineers who are new but eager to use it.
Multi-stream Convolutional Neural Network with Frequency Selection for Robust Speaker Verification
For the diversity of temporal embeddings, we consider feature augmentation with frequency selection, which is to manually segment the full-band of frequency into several sub-bands, and the feature extractor of each stream can select which sub-bands to use as target frequency domain.
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