3 accepted papers
In this work, the generalization of speech enhancement algorithms based on deep neural networks (DNNs) for training datasets that differ in size and diversity is analyzed. For this, we compare noise aware training (NAT) features and signal-to-noise ratio (SNR) based noise aware training (SNR-NAT) fe…
Due to the low computational complexity and the low memory consumption, first-order recursive smoothing is a technique often applied to estimate the mean of a random process. For instance, recursive smoothing is used in noise power estimators where adaptively changing smoothing factors are used inst…
The robustness of speech recognizers towards noise can be increased by normalizing the statistical moments of the Mel-frequency cepstral coefficients (MFCCs), e. g. by using cepstral mean normalization (CMN) or cepstral mean and variance normalization (CMVN). The necessary statistics are estimated o…