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检索条件"主题词=denoising autoencoder"

共 346 条记录，以下是331-340 订阅

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Bengali Handwritten Numeric Character Recognition using denoising autoencoders

Bengali Handwritten Numeric Character Recognition using Deno...

引用

IEEE International Conference on Engineering and Technology (ICETECH)

作者： Pal, Arghya Goa Univ Dept CST Taleigao Plateau 403206 Goa India

ISBN: (纸本)9781479918546

This work describes the recognition of Bengali Handwritten Numeral Recognition using Deep denoising autoencoder using Multilayer Perceptron (MLP) trained through backpropagation algorithm (DDA). To bring the weights of the DDA to some good solution a layer wise pre-training is done with denoising autoencoders. denoising autoencoders using MLP trained through backpropagation algorithm are made by introducing masking noise at input to the autoencoder to capture meaningful information while hidden layers are remain untouched at pre-training. Those pre-trained denoising autoencoders are then stacked to build a DDA. DDA is then converted to a Deep Classifier (DC) by using a final output layer. After a final fine-tune best DC is selected to discriminate classes. Performance of the DC using DDA is compared with the Deep autoencoder using MLP trained through backpropagation (DA) and Support Vector Machines (SVM). From the experiment it is evident that recognition performance of DDA that is 98.9% is higher than DA and SVM those are 97.3% and 97%. Using their performance at validation set results are further combined to build a Hybrid Recognizer that gives a performance of 99.1%

关键词： denoising autoencoder MLP Deep Network Handwriting Numeral Recognition

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Recognition of online Handwritten Bangla Characters using Hierarchical System with denoising autoencoders 4

Recognition of online Handwritten Bangla Characters using Hi...

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INTERNATIONAL CONFERENCE ON COMPUTATION OF POWER, ENERGY, INFORMATION AND COMMUNICATION

作者： Pal, Arghya Pawar, J. D. Goa Univ Dept CST Taleigao 403206 Goa India

ISBN: (纸本)9781467365246

This work describes the recognition of online handwritten Bengali characters using Deep denoising autoencoder with Multilayer Perceptron (MLP) trained through backpropagation algorithm [I]. Initial pre-training has been done to the denoising autoencoder with MLP trained through backpropagation algorithm, to bring the weights of the Deep network to some good solution and then pre-trained denoising autoencoders are stacked to form a Deep denoising autoencoder (DDA). A final classification layer makes DDA to a Deep Classifier (DC) followed by a final fine-tune that gives the best classifier for the job of classification of Bengali characters. The overall system is hierarchical in nature and the system has been trained in two phase where the first phase has trained a broad classifier and in the second phase class specific recognizer has been trained. At the testing phase in this hierarchical approach, first a broad classifier has been used to recognize broad classes like Vowel, Consonant, Special Symbol and Numeral for a novel test sample. Once the broad class gets recognized then a class specific recognizer has been used to recognize the exact character the test sample belongs. Recognition performance of the hierarchical system is 93.12%.

关键词： MLP denoising autoencoder Deep Network Bengali Character Classification

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Deep Difference Representation Learning for Multi-spectral Imagery Change Detection

Deep Difference Representation Learning for Multi-spectral I...

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2016 5th International Conference on Advanced Materials and Computer Science(ICAMCS 2016)

作者： Hui Zhang Puzhao Zhang Department of Integrated Circuit Design and Integrated System School of MicroelectronicsXidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University

Change detection is an ongoing hot topic in multi-spectral imagery applications,how to exploit the available spectral information effectively for change detection is still an open *** the noise interference and redundancy of multi-spectral imagery,it is important and necessary to learn more abstract and robust feature from raw spectrums for change detection *** this paper,a deep difference representation learning model is proposed for multi-spectral change *** this model,two stacked denoising autoencoders are established,one for learning more abstract features from raw spectrums blocks,and the other for learning difference representations from the stacked change *** former is used to weaken noise interference and reduce redundancy,while the latter has the ability to highlight changes and suppress unchanged *** experimental results on real multi-spectral data demonstrate the feasibility,effectiveness and robustness of the proposed deep difference representation learning model on multi-spectral change detection task.

关键词： Change Detection Multi-Spectral Imagery Difference Representation denoising autoencoder Deep Learning

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Vibration Based Multimodal Feature Learning Framework For Wind Turbine Gearbox Fault Diagnosis

Vibration Based Multimodal Feature Learning Framework For Wi...

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The 29th International Congress on Condition Monitoring and Diagnostic Engineering Management

作者： Guoqian Jiang Ping Xie Xiao Wang Qun He Meng Chen School of Electrical Engineering Yanshan University

Fault diagnosis of wind turbine gearboxes has attracted great attention,and it is still a quite challenging task to extract effective features from the raw vibration signals to achieve accurate and reliable fault ***,various vibration based features from time,frequency and time-frequency representation are extracted for further fault recognition achieved by *** different representations can be viewed as a modality of the vibration *** different modalities can provide complementary information for diagnosis,and may have different sensitivities to health ***,there is need to fuse them to enhance fault diagnosis *** by the idea of multimodal deep learning(MDL),this paper presents a multimodal vibration feature learning and fusion framework,with the aim to fuse multimodal homologous features derived from the single channel gearbox vibration signal using deep autoencoder *** framework mainly includes unsupervised compact vibration feature learning from each modality and unified representation learning by fusing learned multimodal features followed by a simple classifier for fault *** on a wind turbine gearbox test rig show that the proposed framework achieves best diagnosis accuracy compared to single modality based approaches.

关键词： wind turbine gearbox vibration signal fault diagnosis multimodal deep learning deep autoencoders denoising autoencoder

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SEPARATION MATRIX OPTIMIZATION USING ASSOCIATIVE MEMORY MODEL FOR BLIND SOURCE SEPARATION 23

SEPARATION MATRIX OPTIMIZATION USING ASSOCIATIVE MEMORY MODE...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Omachi, Motoi Ogawa, Tetsuji Kobayashi, Tetsunori Fujieda, Masaru Katagiri, Kazuhiro Waseda Univ Dept Comp Sci Tokyo Japan Oki Elect Ind Co Ltd Minato Tokyo Japan

ISBN: (纸本)9780992862633

A source signalis estimated using an associative memory model (AMM) and used for separation matrix optimization in linear blind source separation (BSS) to yield high quality and less distorted speech. Linear-filtering-based BSS, such as independent vector analysis (IVA), has been shown to he effective in sound source separation while avoiding non-linear signal distortion. This technique, however, requires several assumptions of sound sources being independent and generated from non-Gaussian distribution. We propose a method for estimating a linear separation matrix without, any assumptions about the sources by repeating the following two steps: estimating non-distorted reference signals by using an AMM and optimizing the separation matrix to minimize an error between the estimated signal and reference signal. Experimental comparisons carried out in simultaneous speech separation suggest that the proposed method can reduce the residual distortion caused by IVA.

关键词： convolutional neural network denoising autoencoder associative memory model linear filtering blind source separation

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Frequency Offset Correction in Single Sideband(SSB) Speech by Deep Neural Network for Speaker Verification 16

Frequency Offset Correction in Single Sideband(SSB) Speech b...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Xing, Hua Liu, Gang Hansen, John H. L. Univ Texas Dallas Ctr Robust Speech Syst Richardson TX 75083 USA

ISBN: (纸本)9781510817906

Communication system mismatch represents a major influence for loss in speaker recognition performance. This paper considers a type of nonlinear communication system mismatch- modulation/demodulation (Mod/DeMod) carrier drift in single side band (SSB) speech signals. We focus on the problem of estimating frequency offset in SSB speech in order to improve speaker verification performance of the drifted speech. Based on a two-step framework from previous work, we propose using a multi-layered neural network architecture, stacked denoising autoencoder (SDA), to determine the unique interval of the offset value in the first step. Experimental results demonstrate that the SDA based system can produce up to a +16.1% relative improvement in frequency offset estimation accuracy. A speaker verification evaluation shows a +65.9% relative improvement in EER when SSB speech signal is compensated with the frequency offset value estimated by the proposed method.

关键词： frequency offset single sideband speaker verification denoising autoencoder

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Bottleneck Features from SNR-Adaptive denoising Deep Classifier for Speaker Identification

Bottleneck Features from SNR-Adaptive Denoising Deep Classif...

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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Tan, Zhili Mak, Man-Wai Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9789881476807

In this paper, we explore the potential of using deep learning for extracting speaker-dependent features for noise robust speaker identification. More specifically, an SNR-adaptive denoising classifier is constructed by stacking two layers of restricted Boltzmann machines (RBMs) on top of a denoising deep autoencoder, where the top-RBM layer is connected to a soft-max output layer that outputs the posterior probabilities of speakers and the top-RBM layer outputs speaker-dependent bottleneck features. Both the deep autoencoder and RBMs are trained by contrastive divergence, followed by backpropagation fine-tuning. The autoencoder aims to reconstruct the clean spectra of a noisy test utterance using the spectra of the noisy test utterance and its SNR as input. With this denoising capability, the output from the bottleneck layer of the classifier can be considered as a low-dimension representation of denoised utterances. These frame-based bottleneck features are than used to train an iVector extractor and a PLDA model for speaker identification. Experimental results based on a noisy YOHO corpus show that the bottleneck features slightly outperform the conventional MFCC under low SNR conditions and that fusion of the two features lead to further performance gain, suggesting that the two features are complementary with each other.

关键词： Deep learning Bottleneck features denoising autoencoder speaker identification deep belief networks

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S-VECTOR: A DISCRIMINATIVE REPRESENTATION DERIVED FROM I-VECTOR FOR SPEAKER VERIFICATION 23

S-VECTOR: A DISCRIMINATIVE REPRESENTATION DERIVED FROM I-VEC...

引用

23rd European Signal Processing Conference (EUSIPCO)

作者： Isik, Yusuf Ziya Erdogan, Hakan Sarikaya, Ruhi TUBITAK BILGEM Gebze Turkey Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9780992862633

Representing data in ways to disentangle and factor out hidden dependencies is a critical step in speaker recognition systems. In this work, we employ deep neural networks (DNN) as a feature extractor to disentangle and emphasize the speaker factors from other sources of variability in the commonly used i-vector features. denoising autoencoder based unsupervised pre-training, random dropout fine-tuning, and Nesterov accelerated gradient based momentum is used in DNN training. Replacing the i-vectors with the resulting speaker vectors (s-vectors), we obtain superior results on NIST SRE corpora on a wide range of operating points using probabilistic linear discriminant analysis (PLDA) back-end.

关键词： speaker verification denoising autoencoder random dropout

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Loop Closure Detection for Visual SLAM Systems Using Deep Neural Networks 34

Loop Closure Detection for Visual SLAM Systems Using Deep Ne...

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34th Chinese Control Conference (CCC)

作者： Gao, Xiang Zhang, Tao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789881563897

The detection of loop closure is of essential importance in visual simultaneous localization and mapping systems. It can reduce the accumulating drift of localization algorithms if the loops are checked correctly. Traditional loop closure detection approaches take advantage of Bag-of-Words model, which clusters the feature descriptors as words and measures the similarity between the observations in the word space. However, the features are usually designed artificially and may not be suitable for data from new-coming sensors. In this paper a novel loop closure detection approach is proposed that learns features from raw data using deep neural networks instead of common visual features. We discuss the details of the method of training neural networks. Experiments on an open dataset are also demonstrated to evaluate the performance of the proposed method. It can be seen that the neural network is feasible to solve this problem.

关键词： Simultaneous Localization and Mapping Loop Closure Detection Deep Neural Networks denoising autoencoder

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SEPARATION MATRIX OPTIMIZATION USING ASSOCIATIVE MEMORY MODEL FOR BLIND SOURCE SEPARATION

SEPARATION MATRIX OPTIMIZATION USING ASSOCIATIVE MEMORY MODE...

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European Signal Processing Conference

作者： Motoi Omachi Tetsuji Ogawa Tetsunori Kobayashi Masaru Fujieda Kazuhiro Katagiri Department of the computer science Waseda University Oki Electric Industry Co. Ltd.

ISBN: (纸本)9781479988518

A source signal is estimated using an associative memory model (AMM) and used for separation matrix optimization in linear blind source separation (BSS) to yield high quality and less distorted speech. Linear-filtering-based BSS, such as independent vector analysis (IVA), has been shown to be effective in sound source separation while avoiding non-linear signal distortion. This technique, however, requires several assumptions of sound sources being independent and generated from non-Gaussian distribution. We propose a method for estimating a linear separation matrix without any assumptions about the sources by repeating the following two steps: estimating non-distorted reference signals by using an AMM and optimizing the separation matrix to minimize an error between the estimated signal and reference signal. Experimental comparisons carried out in simultaneous speech separation suggest that the proposed method can reduce the residual distortion caused by IVA.

关键词： Convolutional neural network denoising autoencoder Associative memory model Linear filtering Blind source separation

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