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检索条件"主题词=denoising autoencoder"

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SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement 17

SNR-Aware Convolutional Neural Network Modeling for Speech E...

引用

17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Fu, Szu-Wei Tsao, Yu Lu, Xugang Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Inst Informat & Commun Technol Kyoto Japan

ISBN: (纸本)9781510833135

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 and accuracy, we propose two SNR-aware algorithms for CNN modeling. The first algorithm employs a multi -task learning (MTL) framework, in which restoring clean speech and estimating SNR level are formulated as the main and the secondary tasks, respectively, given the noisy speech input. The second algorithm is an SNR adaptive denoising, in which the SNR level is explicitly predicted in the first step, and then an SNR-dependent CNN model is selected for denoising. Experiments were carried out to test the two SNR-aware algorithms for CNN modeling. Results demonstrate that CNN with the two proposed SNR-aware algorithms outperform the deep neural network counterpart in terms of standardized objective evaluations when using the same number of layers and nodes. Moreover, the SNR-aware algorithms can improve the de noising performance with unseen SNR levels, suggesting their promising generalization capability for real-world applications.

关键词： speech enhancement convolutional neural network denoising autoencoder multi-task learning

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Front-end Feature Compensation and denoising for Noise Robust Speech Emotion Recognition 20

Front-end Feature Compensation and Denoising for Noise Robus...

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Interspeech Conference

作者： Chakraborty, Rupayan Panda, Ashish Pandharipande, Meghna Joshi, Sonal Kopparapu, Sunil Kumar TCS Res & Innovat Mumbai Maharashtra India

Front-end processing is one of the ways to impart noise robustness to speech emotion recognition systems in mismatched scenarios. Here, we implement and compare different front-end robustness techniques for their efficacy in speech emotion recognition. First, we use a feature compensation technique based on the Vector Taylor Series (VTS) expansion of noisy Mel-Frequency Cepstral Coefficents (MFCCs). Next, we improve upon the feature compensation technique by using the VTS expansion with auditory masking formulation. We have also looked into the applicability of 10(th) -root compression in MFCC computation. Further, a Time Delay Neural Network based denoising autoencoder (TDNN-DAE) is implemented to estimate the clean MFCCs from the noisy MFCCs. These techniques have not been investigated yet for their suitability to robust speech emotion recognition task. The performance of these front-end techniques are compared with the Non-Negative Matrix Factorization (NMF) based front-end. Relying on extensive experiments done on two standard databases (EmoDB and IEMOCAP), contaminated with 5 types of noise, we show that these techniques provide significant performance gain in emotion recognition task. We also show that along with front-end compensation, applying feature selection to non-MFCC high-level descriptors results in better performance.

关键词： Speech emotion recognition noise robustness denoising autoencoder feature compensation auditory masking

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An Effective Deep Learning Based Scheme for Network Intrusion Detection 24

An Effective Deep Learning Based Scheme for Network Intrusio...

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24th International Conference on Pattern Recognition (ICPR)

作者： Zhang, Hongpo Wu, Chase Q. Gao, Shan Wang, Zongmin Xu, Yuxiao Liu, Yongpeng State Key Lab Math Engn & Adv Comp Zhengzhou Henan Peoples R China Zhengzhou Univ Cooperat Innovat Ctr Internet Healthcare Zhengzhou Henan Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Hangzhou DPtech Technol Co Ltd Dept Res & Dev Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538637883

Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks.

关键词： Intrusion detection system denoising autoencoder feature selection deep learning

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Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequence Models for Extremely Low-Resource NMT 7

Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequenc...

引用

Moratuwa Engineering Research Conference (MERCon) / 7th International Multidisciplinary Engineering Research Conference

作者： Thillainathan, Sarubi Ranathunga, Surangika Jayasena, Sanath Univ Moratuwa Dept Comp Sci & Engn Katubedda Sri Lanka

ISBN: (纸本)9781665437530

Neural Machine Translation (NMT) tends to perform poorly in low-resource language settings due to the scarcity of parallel data. Instead of relying on inadequate parallel corpora, we can take advantage of monolingual data available in abundance. Training a denoising self-supervised multilingual sequence-to-sequence model by noising the available large scale monolingual corpora is one way to utilize monolingual data. For a pair of languages for which monolingual data is available in such a pre-trained multilingual denoising model, the model can be fine-tuned with a smaller amount of parallel data from this language pair. This paper presents fine-tuning self-supervised multilingual sequence-to-sequence pre-trained models for extremely low-resource domain-specific NMT settings. We choose one such pre-trained model: mBART. We are the first to implement and demonstrate the viability of non-English centric complete fine-tuning on multilingual sequence-to-sequence pretrained models. We select Sinhala, Tamil and English languages to demonstrate fine-tuning on extremely low-resource settings in the domain of official government documents. Experiments show that our fine-tuned mBART model significantly outperforms state-of-the-art Transformer based NMT models in all pairs in all six bilingual directions, where we report a 4.41 BLEU score increase on Tamil -> Sinhala and a 2.85 BLUE increase on Sinhala -> Tamil translation.

关键词： neural machine translation pre-trained models fine-tuning denoising autoencoder low-resource languages

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3D Reconstruction from 2D Cerebral Angiograms as a Volumetric denoising Problem 18th

3D Reconstruction from 2D Cerebral Angiograms as a Volumetri...

引用

18th International Symposium on Visual Computing (ISVC)

作者： Wu, Sean Kaneko, Naoki Mendoza, Steve Liebeskind, David S. Scalzo, Fabien Pepperdine Univ Keck Data Sci Inst Malibu CA 90265 USA UCLA Dept Intervent Neuroradiol Los Angeles CA 90095 USA UCLA Dept Neurol Los Angeles CA 90095 USA

ISBN: (纸本)9783031479687;9783031479694

Accurately capturing the 3D geometry of the brain's blood vessels is critical in helping neuro-interventionalists to identify and treat neurovascular disorders, such as stroke and aneurysms. Currently, the gold standard for obtaining a 3D representation of angiograms is through the process of 3D rotational angiography, a timely process requiring expensive machinery, which is also associated with high radiation exposure to the patient. In this research, we propose a new technique for reconstructing 3D volumes from 2D angiographic images, thereby reducing harmful X-ray radiation exposure. Our approach involves parameterizing the input data as a back-projected noisy volume from the images, which is then fed into a 3D denoising autoencoder. Through this method, we have achieved clinically relevant reconstructions with varying amounts of 2D projections from 49 patients. Additionally, our 3D denoising autoencoder outperformed previous generative models in biplane reconstruction by 15.51% for intersection over union (IOU) and 3.5% in pixel accuracy due to keeping a semi-accurate input with back projection. This research highlights the significant role of back-projection in achieving relative visual correspondence in the input space to reconstruct 3D volumes from 2D angiograms. This approach has the potential to be deployed in future neurovascular surgery, where 3D volumes of the patient's brain blood vessels can be visualized with less X-ray radiation exposure time.

关键词： 3D Reconstruction Deep Learning denoising autoencoder Rotational Angiography Cerebral Angiograms

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DCA-CLA: A scRNA-seq Classification Framework based on Deep Count autoencoder

DCA-CLA: A scRNA-seq Classification Framework based on Deep ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wu, Yulun Guo, Yanming Li, Jian Lao, Songyang Guo, Jinlin Wang, Haoran Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9780738133669

Identifying cell types is crucial for single-cell RNA sequencing (scRNA-seq) analysis and can be potentially utilized to understand high-level biological processes. Supervised models based on neural networks have recently been successfully applied in the scRNA-seq cell type classification problem and achieved promising results. While most existing works directly use the raw or transformed data, we argue that the original data are too sparse and high-dimensional, and extracting their effective low-dimensional features can better train downstream classifiers, thereby improving the cell type classification performance. In this paper, we propose a novel framework, named Deep Count autoencoder-based Classifier (DCA-CLA), to leverage the discriminative low-dimensional features for classification. Specifically, DCA-CLA first denoises the original count matrix and extracts the data features from the hidden layer using a deep count autoencoder module, then it feeds these bottleneck features into the classifier network to train the learnable parameters and test the performance. Experimental results on eight separate datasets and four pairs of datasets demonstrate that the proposed DCA-CLA framework achieves competitive performance over the state-of-the-art frameworks.

关键词： denoising autoencoder single-cell RNA sequencing classification supervised learning bioinformatics

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Author Identification using Deep Learning 15

Author Identification using Deep Learning

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15th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Mohsen, Ahmed M. El-Makky, Nagwa M. Ghanem, Nagia Alexandria Univ Fac Engn Alexandria Egypt

ISBN: (纸本)9781509061679

Authorship identification is the task of identifying the author of a given text from a set of suspects. The main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In this paper, deep learning is used for feature extraction of documents represented using variable size character n-grams. We apply A Stacked denoising autoencoder (SDAE) for extracting document features with different settings, and then a support vector machine classifier is used for classification. The results show that the proposed system outperforms its counterparts

关键词： denoising autoencoder author identification deep learning

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Reading Imagined Letter Shapes from the Mind's Eye Using Real-time 7 Tesla fMRI 10

Reading Imagined Letter Shapes from the Mind's Eye Using Rea...

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10th International Winter Conference on Brain-Computer Interface (BCI)

作者： Goebel, Rainer van Hoof, Rick Bhat, Salil Luhrs, Michael Senden, Mario Maastricht Univ Dept Cognit Neurosci Maastricht Netherlands

ISBN: (纸本)9781665413374

We present a 7 Tesla fMRI proof-of-concept study of the first letter speller BCI that decodes imagined letter shapes from activity patterns in early visual cortical areas. New tools are developed to enable real-time population receptive field retinotopic mapping for encoding and decoding. Using two different letter shapes (H and T), classification performance of generated activity patterns during imagery reaches 80% accuracy in each individual. Using a denoising autoencoder, recognizable letter shapes could be reconstructed and displayed as feedback to participants in the scanner.

关键词： fMRI letter speller imagery population receptive fields denoising autoencoder

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Predicting Unmeasured Region of the Efficiency Map of a Speed Reducer Using a denoising Auto-encoder 3

Predicting Unmeasured Region of the Efficiency Map of a Spee...

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3rd International Conference on Computational Intelligence and Applications (ICCIA)

作者： Shin, Crino Jin, Yongsik Jeong, Seunghyun Yun, Jongpil Korea Inst Ind Technol KITECH Cheonan South Korea Kyungpook Natl Univ Daegu South Korea Kyungpook Natl Univ Dept Elect Engn Daegu South Korea

ISBN: (纸本)9781538695715

This paper presents a Remaining Useful Life (RUL) prediction method for a speed reducer based on denoising auto-encoder (DAE). Constructing the efficiency map of the reducer is an important process for predicting the life span. However, due to the situational constraints that occur, unmeasured intervals hinder the completion of the efficiency map. to solve this problem, we propose a method that can predict and reconstruct an unmeasured interval effectively and reliably by using DAE. In addition, we examine the applicability of the proposed algorithm through experiments that assume various situations.

关键词： denoising autoencoder Predicting unmeasured value Remaining Useful Life

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Reducing Speckle Noise from Ultrasound Images Using an autoencoder Network 28

Reducing Speckle Noise from Ultrasound Images Using an Autoe...

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Karaoglu, Onur Bilge, Hasan Sakir Uluer, Ihsan Karabuk Univ Elekt Elekt Muhendisligi Karabuk Turkey Gazi Univ Elekt Elekt Muhendisligi Ankara Turkey

ISBN: (纸本)9781728172064

Image enhancement aims to obtain a clear image from a noisy image and it also uses for ultrasound images. In the experimental study, unlike classical image enhancement methods, deep learning method was used. Different levels of speckle noise added to the ultrasound images of the brachial plexus, which is known as the large nerve community under the armpit, were tried to be removed with the help of the convolutional denoising autoencoder network, which is one of the deep learning methods. The results obtained from the experimental study were compared with classical methods results and the proposed method was found to be more successful than classical methods.

关键词： image enhancement deep learning ultrasound imaging denoising autoencoder

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