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deep denoising autoencoder for seismic random noise attenuation

GEOPHYSICS 2020年第4期85卷 V367-V376页

作者： Saad, Omar M. Chen, Yangkang Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Zhejiang Peoples R China NRIAG ENSN Lab Seismol Dept Helwan 11731 Egypt

Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data;following this, the pretrained model is used to denoise the field data set in an unsupervised scheme using a new customized loss function. We have assessed the proposed algorithm based on four synthetic data sets and two field examples, and we compare the results with several benchmark algorithms, such as f-x deconvolution (f-x deconv) and the f-x singular spectrum analysis (f-x SSA). As a result, our algorithm succeeds in attenuating the random noise in an effective manner.

关键词： deep learning autoencoder random noise attenuation deep denoising autoencoder local similarity

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Stealthy data integrity attack identification in smart grid networks utilizing deep denoising autoencoder

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Heliyon 2024年第19期10卷 e38470页

作者： Kousar, Anila Ahmed, Saeed Altamimi, Abdullah Kim, Su Min Khan, Zafar A. Department of Electrical Engineering Mirpur University of Science and Technology (MUST) Mirpur AJK 10250 Pakistan Department of Electrical Engineering College of Engineering Majmaah University Al-Majmaah 11952 Saudi Arabia Engineering and Applied Science Research Center Majmaah University Al-Majmaah 11952 Saudi Arabia Department of Electronics Engineering Tech University of Korea (TU Korea) Gyeonggi-do Siheung-si 15073 237 South Korea

Smart grids arose as the largest cyber-physical systems with the integration of sophisticated control, computing, and state-of-the-art communications. Like all cyber-physical systems, the smart grids are vulnerable to malicious cyber assaults due to their enormous dependency on communication networks. Various machine learning-based schemes are being investigated in the industry and academia to develop robust defense mechanisms to counter cyber assaults. However, the curse of high dimensionality, which increases with the escalating evolution of an electric power system, infringes upon the efficiency of machine learning models employed to detect such assaults. To this end, this paper proposes a deep denoising autoencoder (DAE)-based framework for dimensionality reduction that learns salient feature representation for high-dimensional, multi-variant smart grid measurement data collected from the smart grids. The latent space apprehended by DAE is then fed to binary support vector machine (SVM) to determine the assaulted data. Various standard IEEE test cases are employed in simulations. The results show that the proposed scheme learns more robust features that reveal the nonlinear properties exhibited in the smart grid measurements, further leading to improved detection accuracy of the classifier as compared to existing approaches. © 2024 The Authors

关键词： Cyber assaults Cyber-physical systems deep denoising autoencoder Machine learning Smart grids State estimation

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A Bandpass Filter With Multi deep denoising autoencoder for Hearing Applications 15

A Bandpass Filter With Multi Deep Denoising Autoencoder for ...

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15th IEEE International Conference on Signal Processing (ICSP)

作者： Lazim, Raghad Yaseen Wu, Xiaojun Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Shaanxi Normal Univ SNNU Key Lab Modern Teaching Technol Minist Educ Xian 710062 Peoples R China

ISBN: (纸本)9781728144801

Speech enhancement techniques in hearing applications aimed to improve the quality of speech in a noisy environment. deep denoising autoencoder suppresses noise from noise corrupted speech efficiently. Unfortunately, previous applications provide only limited benefits for the enhancement of speech in noisy environments. This paper presents a new approach for the hearing application, which indicates two stages of the bandpass filter and a model composed of three levels of deep denoising autoencoders. In the first stage, the bandpass filter designed to allow signals based on the human cochlea, which then followed by a model of three levels of multilayers deep denoising autoencoder, each which specialized for specific enhancement task of a complete set of tasks. The approach performance measured using the perceptual evaluation of speech quality, hearing aid sound quality index, and segmental signal-to-noise ratio. The simulation results prove that the proposed method yielded higher intelligibility and quality in comparison with single-multilayers neural networks.

关键词： Bandpass Filter deep denoising autoencoder Hearing Aids Speech Enhancement Noise Reduction

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Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Lee, Sangmin Kim, Hae Joong Kim, Seoung Bum Korea Univ Dept Ind Management Engn 145 Anam Ro Seoul 02841 South Korea Samsung Elect Mat Handling Automat Grp Samsungjeonja Ro 1 Hwaseong Si 18448 Gyeonggi Do South Korea

deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study, we propose using a DDAE to address a dispatching rule selection problem that represents a major problem in semiconductor manufacturing. Recently, the significance of dispatching systems for storage allocation has become more apparent because operational issues lead to transfer inefficiency, resulting in production losses. Further, recent approaches have overlooked the possibility of a class imbalance problem in predicting the best dispatching rule. The main purpose of this study is to examine DDAE-based predictive control of the storage dispatching systems to reduce idle machines and production losses. We conducted an experimental evaluation to compare the predictive performance of DDAE with those of five other novelty detection algorithms. Finally, we compared our adaptive approach with the optimization and existing heuristic approaches to demonstrate the effectiveness and efficiency of the proposed method. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of machine utilizations and throughputs. (C) 2019 Elsevier B.V. All rights reserved.

关键词： deep denoising autoencoder Novelty detection Dispatching rule selection Storage allocation Class imbalance problem

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Whispered Speech Recognition Using deep denoising autoencoder and Inverse Filtering

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2017年第12期25卷 2313-2322页

作者： Grozdic, Dorde T. Jovicic, Slobodan T. Univ Belgrade Sch Elect Engn Belgrade 11120 Serbia Life Act Adv Ctr Lab Forens Acoust & Phonet Belgrade 11000 Serbia

Due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition (ASR) systems trained on neutral speech degrades significantly when whisper is applied. In order to deeply analyze this mismatched train/test situation and to develop an efficient way for whisper recognition, this study first analyzes acoustic characteristics of whispered speech, addresses the problems of whispered speech recognition in mismatched conditions, and then proposes a new robust cepstral features and preprocessing approach based on deep denoising autoencoder (DDAE) that enhance whisper recognition. The experimental results confirm that Teager-energy-based cepstral features, especially TECCs, are more robust and better whisper descriptors than traditional Mel-frequency cepstral coefficients (MFCC). Further detailed analysis of cepstral distances, distributions of cepstral coefficients, confusion matrices, and experiments with inverse filtering, prove that voicing in speech stimuli is the main cause of word misclassification in mismatched train/test scenarios. The new framework based on DDAE and TECC feature, significantly improves whisper recognition accuracy and outperforms traditional MFCC and GMM-HMM (Gaussian mixture density-Hidden Markov model) baseline, resulting in an absolute 31% improvement of whisper recognition accuracy. The achieved word recognition rate in neutral/whisper scenario is 92.81%.

关键词： Automatic speech recognition deep denoising autoencoder inverse filtering Teager-energy operator whispered speech recognition

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An online learning framework for aero-engine sensor fault detection isolation and recovery

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 162卷

作者： Wang, Kun He, Ai Liu, Jiashuai Zhou, Qifan Hu, Zhongzhi Tsinghua Univ Inst Aero Engine Beijing 100084 Peoples R China Northwestern Polytech Univ Sch Power & Energy Xian 710129 Peoples R China

Accurate and reliable sensor data are critical for the safe operation of modern aero-engine control systems. However, maintaining the accuracy and robustness of fault diagnosis models throughout the engine lifecycle is particularly challenging, especially under conditions of gradual degradation. To address these challenges, this paper proposes a novel Fault Detection, Isolation, and Recovery (FDIR) framework. The framework utilizes a deep denoising autoencoder (DDAE) for fault detection, a multi-model strategy for fault isolation, and a dualtask learning framework for fault signal recovery, ensuring system integrity and continuous operation. Additionally, an online update mechanism based on distribution mean shifts is introduced, integrating parameter regularization and memory replay to prevent catastrophic forgetting and enhance adaptability. Experimental results demonstrate that the proposed framework achieves high-precision FDIR under both non-degraded and degraded conditions, exhibiting superior robustness and adaptability. By combining data-driven methods with adaptive online learning mechanisms, this work provides a scalable and reliable solution for aero-engine sensor fault diagnosis. It not only enhances the operational safety and efficiency of complex, data-intensive systems but also contributes to advancing the state of the art in this field.

关键词： Aero-engine Sensor fault detection Isolation and recovery deep denoising autoencoder Multiple model architecture Dual-task learning Online learning

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A novel unsupervised approach based on the hidden features of deep denoising autoencoders for COVID-19 disease detection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 192卷 116366-116366页

作者： Scarpiniti, Michele Ahrabi, Sima Sarv Baccarelli, Enzo Piazzo, Lorenzo Momenzadeh, Alireza Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Via Eudossiana 18 I-00184 Rome Italy

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a deep denoising Convolutional autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

关键词： deep denoising autoencoder Convolutional autoencoders Feature learning Computed Tomography (CT) Coronavirus disease 2019 COVID-19 Pneumonia Anomaly detection

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Boundary-Preserved deep denoising of Stochastic Resonance Enhanced Multiphoton Images

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022年 10卷 1页

作者： Niu, Sheng-Yong Guo, Lun-Zhang Li, Yue Zhang, Zhiming Wang, Tzung-Dau Liu, Kai-Chun Li, You-Jin Tsao, Yu Liu, Tzu-Ming Acad Sinica Res Ctr Informat Technol Innovat CITI Taipei 11529 Taiwan Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92093 USA Natl Taiwan Univ Dept Biomed Engn Taipei 10617 Taiwan Univ Macau Fac Hlth Sci Inst Translat Med Taipa Macao Peoples R China Univ Macau Minist Educ Frontiers Sci Ctr Precis Oncol Taipa Macao Peoples R China Natl Taiwan Univ Hosp Coll Med Cardiovasc Ctr Taipei 10002 Taiwan Natl Taiwan Univ Hosp Coll Med Dept Internal Med Div Cardiol Taipei 10002 Taiwan Chung Yuan Christian Univ Dept Elect Engn Taoyuan 32023 Taiwan

Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images;however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable. Method: We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images. Results: The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions. Conclusions: The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising. Clinical Impact: The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.

关键词： Microscopy Noise reduction Imaging Photonics Fluorescence Optical filters Microprocessors Third harmonic generation three-photon fluorescence deep denoising autoencoder

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A real-world noise removal with wavelet speech feature

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2020年第3期23卷 683-693页

作者： Chiluveru, Samba Raju Tripathy, Manoj Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India

Real-world noise signals are non-stationary, and these signals are a mixture of more than one non-stationary noise signal. Most of the conventional speech enhancement algorithms (SEAs) focus primarily on a single noise corrupted speech signal, and it is far from real-world environments. In this article, we discuss speech enhancement in real-world environments with a new speech feature. The novelty of this article is three-fold, (1) The proposed model analyzed in real-world environments. (2) The proposed model uses a discrete wavelet transform (DWT) coefficients as input features. (3) The proposed deep denoising autoencoder (DDAE) designed experimentally. The result of the proposed feature compares with conventional speech features like FFT-Amplitude, Log-Magnitude, Mel frequency cepstral coefficients (MFCCs), and the Gammatone filter cepstral coefficients (GFCCs). The performance of the proposed method compared with conventional speech enhancement methods. The enhanced signal evaluated with speech quality measures, like, perceptual evaluation speech quality (PESQ), weighted spectral slope (WSS), and Log-likelihood ratio (LLR). Similarly, speech intelligibility measured with short-time objective intelligibility (STOI). The results show that the proposed SEA model with the DWT feature improves quality and intelligibility in all real-world environmental Signal-to-Noise ratio (SNR) conditions.

关键词： Speech enhancement Discrete wavelet transform coefficients deep denoising autoencoder Short-time objective intelligibility Perceptual evaluation speech quality

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SELF-SUPERVISED denoising autoencoder WITH LINEAR REGRESSION DECODER FOR SPEECH ENHANCEMENT

SELF-SUPERVISED DENOISING AUTOENCODER WITH LINEAR REGRESSION...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zezario, Ryandhimas E. Hussain, Tassadaq Lu, Xugang Wang, Hsin-Min Tsao, Yu Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Taiwan Int Grad Program Social Network & Human Ct Inst Informat Sci Taipei Taiwan Natl Inst Informat & Commun Technol Koganei Tokyo Japan Acad Sinica Sti Tute Informat Sci Taipei Taiwan

ISBN: (纸本)9781509066315

Nonlinear spectral mapping-based models based on supervised learning have successfully applied for speech enhancement. However, as supervised learning approaches, a large amount of labelled data (noisy-clean speech pairs) should be provided to train those models. In addition, their performances for unseen noisy conditions are not guaranteed, which is a common weak point of supervised learning approaches. In this study, we proposed an unsupervised learning approach for speech enhancement, i.e., denoising autoencoder with linear regression decoder (DAELD) model for speech enhancement. The DAELD is trained with noisy speech as both input and target output in a self-supervised learning manner. In addition, with properly setting a shrinkage threshold for internal hidden representations, noise could be removed during the reconstruction from the hidden representations via the linear regression decoder. Speech enhancement experiments were carried out to test the proposed model. Results confirmed that the proposed DAELD could achieve comparable and sometimes even better enhancement performance as compared to the conventional supervised speech enhancement approaches, in both seen and unseen noise environments. Moreover, we observe that higher performances tend to achieve by DAELD when the training data cover more diverse noise types and signal-to-noise-ratio (SNR) levels.

关键词： speech enhancement deep denoising autoencoder unsupervised learning

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