Denoising autoencoder
Environment dependent denoising autoencoder for distant talking speech recognition
... the denoising autoencoder (DAE), one type of DNN, has been shown to be effective in many noise reduction applications because higher-level representations and increased flexibility of the feature mapping ...
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A Power System Transient Stability Assessment Model Based on Stacked Denoising Autoencoder
... Abstract. Since the grid data is not always “perfect”, there are often cases where data is missing or contains noise. In order to solve the above problems and further improve the generalization ability of the power ...
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Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification
... domain denoising autoencoder (DAE)-based dereverberation are presented for distant-talking speaker identification, and a combination of these two approaches is ...
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Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder
... the denoising autoencoder (DAE) [63], which minimizes the error in reconstructing the input from a stochastically corrupted transformation of the ...this denoising autoencoder is to take a ...
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Detection of Text based Cyberbullying using Semantic Enhanced Marginalized Denoising Autoencoder Learning Model
... stacked denoising autoencoder ...stacked denoising autoencoders (mSDA), which adopts linear instead of nonlinear projection to accelerate training and marginalizes infinite noise distribution in ...
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Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder
... In this paper, we propose an unsupervised do- main adaptation for Word Sense Disambigua- tion (WSD) using Stacked Denoising Autoen- coder (SdA). SdA is an unsupervised learn- ing method of obtaining the abstract ...
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Multi Domain Sentiment Relevance Classification with Automatic Representation Learning
... Sentiment relevance (SR) aims at identify- ing content that does not contribute to sen- timent analysis. Previously, automatic SR classification has been studied in a limited scope, using a single domain and feature ...
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The optimally designed autoencoder network for compressed sensing
... sparse denoising autoencoder compressed sensing (SSDAE_CS) model, which mainly consists of an encoder sub-network and a decoder sub-network, is proposed and analyzed in this ...
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Deep Neural Networks for Recommender Systems
... is Denoising Autoencoders (DAE) where noise is added to some fraction of ...The Denoising Autoencoder then can be trained to impute the missing ...
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A Review On Recommendation Systems Using Deep Learning
... networks. In [40] the product purchasing rate of users is determined after mentioning those products in tweets of Twitter. The proposed algorithm outperforms the best baseline by up to 46.7%. Another variation of ...
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Implementation of NLM for Denoising of MRI Images by Using FPGA Mechanism
... the important features in brain MR images. Furthermore, this paper presents a novel Bayesian-inference algorithm on MRFs, namely iterated conditional entropy reduction (ICER). This paper also extends the application of ...
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A Novel Image Denoising By Targeted External Databases
... image denoising should be target- oriented, ...image denoising, we present algorithms and corresponding simulations of using targeted databases for optimal linear denoising filter ...
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A New Denoising System for SONAR Images
... SAR denoising method proposed in [24]. The two denoising algorithms proposed in [24] use the ...first denoising algorithm proposed in [24] performs a local linear minimum mean square error (LLMMSE) ...
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Denoising stacked autoencoders for transient electromagnetic signal denoising
... alyze the known geological features carefully and apply the model according to the actual geological conditions before using our method. And this method has a good generalization for different collection points of the ...
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Implementation of NLM for Denoising of MRI Images by Using FPGA
... Mean, Wiener and Average filtering methods were compared to the non-local means method using four different criteria. 1) Visual quality comparison, 2) mean square error (MSE) comparison, 3) Peak Signal to Noise Ratio ...
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Automatic and efficient denoising of bioacoustics recordings using MMSE STSA
... after denoising, and a modified peak signal to noise ratio, which considers the ratio between the maximum value and mean squared error of the ...simple denoising methods such as low and high-pass ...the ...
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Adaptive Wavelet Packet Decomposition for Efficient Image Denoising By Using NeighSure Shrink Method
... Abstract — The search for efficient image denoising methods is still a valid challenge.This project presents adaptive wavelet packet(WP) decomposition along with the improved neighboring window method, which can ...
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Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal
... In recent years, Konstantin, Dragomiretskiy et al. proposed variational modal decomposition [18], which is essentially composed of several adaptive Wiener filters and has good noise robustness. Compared with EMD, VMD has ...
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A Robust Image Denoising Technique in the Contourlet Transform Domain
... In this section, we compare the proposed method with other denoising methods. We have performed several experiments to test the proposed algorithms and evaluate its performance with standard images. The ...
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1. Ica & wavelet as a method for speech signal denoising
... unavoidable inStep2.EMD-wavelet-ICA utilizes EMD other than wavelet decomposition to analysis the signal in Step1, hence the of selecting appropriate mother brought to minimum. For simplicity, we mother wavelet in the ...
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