3 accepted papers
Most of the deep learning based speech enhancement methods focus on the modeling of complicated relationship between the noisy speech and the clean speech without the consideration of noise information. In order to cope with various complex noise scenes, we introduce a novel enhancement architecture…
Convolution neural networks (CNNs) have been achieving increasing attention for the artificial bandwidth extension (ABE) task recently. However, these methods use the flipped low-frequency phase to reconstruct speech signals, which may lead to the well-known invalid short-time Fourier Transform (STF…
The data driven speech enhancement approaches using regression-based deep neural network usually result in enormous number of model parameters, which increase the computational load and the difficulty of model training. In order to improve the model efficiency, we propose a densely connected network…