作者:
Saad, Omar M.Chen, YangkangZhejiang 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 autoe...
详细信息
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-denoisingautoencoder (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.
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...
详细信息
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, ...
详细信息
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.
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 sel...
详细信息
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.
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 significan...
详细信息
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%.
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 i...
详细信息
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.
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 ea...
详细信息
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 deepdenoising 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.
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 textur...
详细信息
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 denoisingautoencoder 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 deepdenoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.
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 nois...
详细信息
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.
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 pa...
详细信息
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., denoisingautoencoder 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.
暂无评论