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This paper proposes a signal-to-noise-ratio (SNR) aware convolutional neural network (CNN) model for speech enhancement (SE). Because the CNN model can deal with local temporal-spectral structures of speech signals, it can effectively disentangle the speech and noise signals given the noisy speech signals. In order to enhance the generalization capability(More)
Transplantation of cell sheets including an intact extracellular matrix is one tissue-engineering strategy for tissue regeneration. Temperature-responsive substrates based on poly(N-isopropylacrylamide) (PNIPAAm) have been used to harvest intact cell sheets by temperature change. In this work, we immobilized PNIPAAm on plastic substrates by a UV-activated(More)
In image deblurring, it is important to reconstruct images with small error, high perception quality, and less computational time. In this paper, a blurred image reconstruction algorithm, which is a combination of the Richardson-Lucy (RL) deconvolution approach and a pyramid structure, is proposed. The RL approach has good performance in image(More)
Feature points, such as SIFT, BRISK, ORB, and FREAK, are effective for template matching, pattern recognition, and object alignment. However, since an image usually has 200-4000 feature points and the size of each descriptor is 512 or 256, an efficient way for encoding the descriptors and locations of feature points is required. In this paper, we propose an(More)
This study proposes a fully convolutional network (FCN) model for raw waveform-based speech enhancement. The proposed system performs speech enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which differs from most existing denoising methods that process the magnitude spectrum (e.g., log power spectrum (LPS)) only. Because the fully(More)
This paper presents a novel deep neural network (DNN) based speech enhancement method that aims to enhance magnitude and phase components of speech signals simultaneously. The novelty of the proposed method is two-fold. First, to avoid the difficulty of direct clean phase estimation, the proposed algorithm adopts real and imaginary (RI) spectrograms to(More)
The intrinsic mode function (IMF) derived from empirical mode decomposition (EMD) of the Hilbert-Huang transform process is useful for time-variant signal analysis. In this paper, a novel algorithm for IMF compression is proposed. Instead of recording all of the extreme points, we just encode a part of the extreme points while the locations and amplitudes(More)
The stroke is a very important feature for a character and is helpful for word recognition and handwriting identification. Although thinning algorithms can be applied for stroke extraction, they always suffer from the problems of bifurcation and disconnection. Moreover, since the end points of strokes cannot be preserved by thinning, the stroke length(More)
Stereo image compression is more and more important because of new display technologies and the needs of 3D movies. As a video sequence, a pair of stereo images is very similar to each other. Therefore, there are usually a lot of redundancies between them. To improve the compression efficiency, an effective method to estimate the target image from the(More)
Conventionally, the maximum likelihood (ML) criterion is applied to train a deep belief network (DBN). We present a maximum entropy (ME) learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy of parameters in the DBN allows more effective generalization capability, less bias towards data(More)