SNR-Based Features and Diverse Training Data for Robust DNN-Based Speech Enhancement

7 Apr 2020 Robert Rehr Timo Gerkmann

This paper analyzes the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the chosen features, (2) the size and diversity of the training data, and (3) different network architectures. To address (1), we compare three input features, namely logarithmized noisy periodograms, noise aware training (NAT) and signal-to-noise ratio (SNR) based noise aware training (SNR-NAT)... (read more)

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