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Relevant bibliographies by topics / Neural Mask Estimation

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Academic literature on the topic 'Neural Mask Estimation'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Neural Mask Estimation"

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Om, Chol Nam, Hyok Kwak, Chong Il Kwak, Song Gum Ho, and Hyon Gyong Jang. "Multichannel Speech Enhancement of Target Speaker Based on Wakeup Word Mask Estimation with Deep Neural Network." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5754–59. http://dx.doi.org/10.35444/ijana.2023.15101.

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In this paper, we address a multichannel speech enhancement method based on wakeup word mask estimation using Deep Neural Network (DNN). It is thought that the wakeup word is an important clue for target speaker. We use a DNN to estimate the wakeup word mask and noise mask and apply them to separate the mixed wakeup word signal into target speaker’s speech and background noise. Convolutional Recurrent Neural Network (CRNN) is used to exploit both short and long term time-frequency dependencies of sequences such as speech signals. Generalized Eigen Vector (GEV) beamforming estimates the spatial filter by using the masks to enhance the following speech command of target speaker and reduce undesirable noise. Experiment results show that the proposal provides more robust to noise, so that improves the Signal-to-Noise Ratio (SNR) and speech recognition accuracy.

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Lee, Hyo-Jun, Jong-Hyeon Baek, Young-Kuk Kim, Jun Heon Lee, Myungjae Lee, Wooju Park, Seung Hwan Lee, and Yeong Jun Koh. "BTENet: Back-Fat Thickness Estimation Network for Automated Grading of the Korean Commercial Pig." Electronics 11, no. 9 (April 19, 2022): 1296. http://dx.doi.org/10.3390/electronics11091296.

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For the automated grading of the Korean commercial pig, we propose deep neural networks called the back-fat thickness estimation network (BTENet). The proposed BTENet contains segmentation and thickness estimation modules to simultaneously perform a back-fat area segmentation and a thickness estimation. The segmentation module estimates a back-fat area mask from an input image. Through both the input image and estimated back-fat mask, the thickness estimation module predicts a real back-fat thickness in millimeters by effectively analyzing the back-fat area. To train BTENet, we also build a large-scale pig image dataset called PigBT. Experimental results validate that the proposed BTENet achieves the reliable thickness estimation (Pearson’s correlation coefficient: 0.915; mean absolute error: 1.275 mm; mean absolute percentage error: 6.4%). Therefore, we expect that BTENet will accelerate a new phase for the automated grading system of the Korean commercial pig.

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Bezsonov, Oleksandr, Oleh Lebediev, Valentyn Lebediev, Yuriy Megel, Dmytro Prochukhan, and Oleg Rudenko. "Breed recognition and estimation of live weight of cattle based on methods of machine learning and computer vision." Eastern-European Journal of Enterprise Technologies 6, no. 9 (114) (December 29, 2021): 64–74. http://dx.doi.org/10.15587/1729-4061.2021.247648.

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A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network. To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method. The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal. When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU. The results obtained confirm the effectiveness of the proposed method in solving practical problems.

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Lee, Chang-bok, Han-sung Lee, and Hyun-chong Cho. "Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images." Applied Sciences 13, no. 5 (February 23, 2023): 2896. http://dx.doi.org/10.3390/app13052896.

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Weight information is important in cattle breeding because it can measure animal growth and be used to calculate the appropriate amount of daily feed. To estimate the weight, we developed an image-based method that does not stress cattle and requires no manual labor. From a 2D image, a mask was obtained by segmenting the animal and background, and weights were estimated using a deep neural network with residual connections by extracting weight-related features from the segmentation mask. Two image segmentation methods, fully and weakly supervised segmentation, were compared. The fully supervised segmentation method uses a Mask R-CNN model that learns the ground truth mask generated by labeling as the correct answer. The weakly supervised segmentation method uses an activation visualization map that is proposed in this study. The first method creates a more precise mask, but the second method does not require ground truth segmentation labeling. The body weight was estimated using statistical features of the segmented region. In experiments, the following performance results were obtained: a mean average error of 17.31 kg and mean absolute percentage error of 5.52% for fully supervised segmentation, and a mean average error of 35.91 kg and mean absolute percentage error of 10.1% for the weakly supervised segmentation.

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Guimarães, André, Maria Valério, Beatriz Fidalgo, Raúl Salas-Gonzalez, Carlos Pereira, and Mateus Mendes. "Cork Oak Production Estimation Using a Mask R-CNN." Energies 15, no. 24 (December 17, 2022): 9593. http://dx.doi.org/10.3390/en15249593.

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Cork is a versatile natural material. It can be used as an insulator in construction, among many other applications. For good forest management of cork oaks, forest owners need to calculate the volume of cork periodically. This will allow them to choose the right time to harvest the cork. The traditional method is laborious and time consuming. The present work aims to automate the process of calculating the trunk area of a cork oak from which cork is extracted. Through this calculation, it will be possible to estimate the volume of cork produced before the stripping process. A deep neural network, Mask R-CNN, and a machine learning algorithm are used. A dataset of images of cork oaks was created, where targets of known dimensions were fixed on the trunks. The Mask R-CNN was trained to recognize targets cork regions, and so the area of cork was estimated based on the target dimensions. Preliminary results show that the model presents a good performance in the recognition of targets and trunks, registering a mAP@0.7 of 0.96. After obtaining the mask results, three machine learning models were trained to estimate the cork volume based on the area and biometric parameters of the tree. The results showed that a support vector machine produced an average error of 8.75%, which is within the error margins obtained using traditional methods.

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Hasannezhad, Mojtaba, Zhiheng Ouyang, Wei-Ping Zhu, and Benoit Champagne. "Speech Enhancement With Phase Sensitive Mask Estimation Using a Novel Hybrid Neural Network." IEEE Open Journal of Signal Processing 2 (2021): 136–50. http://dx.doi.org/10.1109/ojsp.2021.3067147.

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Sivapatham, Shoba, Asutosh Kar, Roshan Bodile, Vladimir Mladenovic, and Pitikhate Sooraksa. "A deep neural network-correlation phase sensitive mask based estimation to improve speech intelligibility." Applied Acoustics 212 (September 2023): 109592. http://dx.doi.org/10.1016/j.apacoust.2023.109592.

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Osorio, Kavir, Andrés Puerto, Cesar Pedraza, David Jamaica, and Leonardo Rodríguez. "A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images." AgriEngineering 2, no. 3 (August 28, 2020): 471–88. http://dx.doi.org/10.3390/agriengineering2030032.

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Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning image processing in lettuce crops, and we compared them to visual estimations by experts. One method is based on support vector machines (SVM) using histograms of oriented gradients (HOG) as feature descriptor. The second method was based in YOLOV3 (you only look once V3), taking advantage of its robust architecture for object detection, and the third one was based on Mask R-CNN (region based convolutional neural network) in order to get an instance segmentation for each individual. These methods were complemented with a NDVI index (normalized difference vegetation index) as a background subtractor for removing non photosynthetic objects. According to chosen metrics, the machine and deep learning methods had F1-scores of 88%, 94%, and 94% respectively, regarding to crop detection. Subsequently, detected crops were turned into a binary mask and mixed with the NDVI background subtractor in order to detect weed in an indirect way. Once the weed image was obtained, the coverage percentage of weed was calculated by classical image processing methods. Finally, these performances were compared with the estimations of a set from weed experts through a Bland–Altman plot, intraclass correlation coefficients (ICCs) and Dunn’s test to obtain statistical measurements between every estimation (machine-human); we found that these methods improve accuracy on weed coverage estimation and minimize subjectivity in human-estimated data.

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Song, Junho, Yonggu Lee, and Euiseok Hwang. "Time–Frequency Mask Estimation Based on Deep Neural Network for Flexible Load Disaggregation in Buildings." IEEE Transactions on Smart Grid 12, no. 4 (July 2021): 3242–51. http://dx.doi.org/10.1109/tsg.2021.3066547.

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Rizwan, Tahir, Yunze Cai, Muhammad Ahsan, Noman Sohail, Emad Abouel Nasr, and Haitham A. Mahmoud. "Neural Network Approach for 2-Dimension Person Pose Estimation With Encoded Mask and Keypoint Detection." IEEE Access 8 (2020): 107760–71. http://dx.doi.org/10.1109/access.2020.3001473.

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Dissertations / Theses on the topic "Neural Mask Estimation"

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Chen, Jitong. "On Generalization of Supervised Speech Separation." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492038295603502.

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Narayanan, Arun. "Computational auditory scene analysis and robust automatic speech recognition." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1401460288.

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Garnier, Aurélie. "Dynamiques neuro-gliales locales et réseaux complexes pour l'étude de la relation entre structure et fonction cérébrales." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066562/document.

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L'un des enjeux majeurs actuellement en neurosciences est l'élaboration de modèles computationnels capables de reproduire les données obtenues expérimentalement par des méthodes d'imagerie et permettant l'étude de la relation structure-fonction dans le cerveau. Les travaux de modélisation dans cette thèse se situent à deux échelles et l'analyse des modèles a nécessité le développement d'outils théoriques et numériques dédiés. À l'échelle locale, nous avons proposé un nouveau modèle d'équations différentielles ordinaires générant des activités neuronales, caractérisé et classifié l'ensemble des comportements générés, comparé les sorties du modèle avec des données expérimentales et identifié les structures dynamiques sous-tendant la génération de comportements pathologiques. Ce modèle a ensuite été couplé bilatéralement à un nouveau compartiment modélisant les dynamiques de neuromédiateurs et leurs rétroactions sur l'activité neuronale. La caractérisation théorique de l'impact de ces rétroactions sur l'excitabilité a été obtenue en formalisant l'étude des variations d'une valeur de bifurcation en un problème d'optimisation sous contrainte. Nous avons enfin proposé un modèle de réseau, pour lequel la dynamique des noeuds est fondée sur le modèle local, incorporant deux couplages: neuronal et astrocytaire. Nous avons observé la propagation d'informations différentiellement selon ces deux couplages et leurs influences cumulées, révélé les différences qualitatives des profils d'activité neuronale et gliale de chaque noeud, et interprété les transitions entre comportements au cours du temps grâce aux structures dynamiques identifiées dans les modèles locaux
A current issue in neuroscience is to elaborate computational models that are able to reproduce experimental data recorded with various imaging methods, and allowing us to study the relationship between structure and function in the human brain. The modeling objectives of this work are two scales and the model analysis need the development of specific theoretical and numerical tools. At the local scale, we propose a new ordinary differential equations model generating neuronal activities. We characterize and classify the behaviors the model can generate, we compare the model outputs to experimental data and we identify the dynamical structures of the neural compartment underlying the generation of pathological patterns. We then extend this approach to a new neuro-glial mass model: a bilateral coupling between the neural compartment and a new one modeling the impact of astrocytes on neurotransmitter concentrations and the feedback of these concentrations on neural activity is developed. We obtain a theoretical characterization of these feedbacks impact on neuronal excitability by formalizing the variation of a bifurcation value as a problem of optimization under constraint. Finally, we propose a network model, which node dynamics are based on the local neuro-glial mass model, embedding a neuronal coupling and a glial one. We numerically observe the differential propagations of information according to each of these coupling types and their cumulated impact, we highlight qualitatively distinct patterns of neural and glial activities of each node, and link the transitions between behaviors with the dynamical structures identified in the local models

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Li, Meng-Huan, and 李孟桓. "Robust and Accurate Iris Mask Estimation using Convolutional Neural Network." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/81465880036765346893.

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博士
國立中央大學
資訊工程學系
105
Iris recognition has a lot of applications. A typical iris recognition system has several stages, including acquisition, segmentation, iris mask generation, feature extraction and matching. In order to increase the accuracy of iris recognition, many studies focus on iris segmentation, feature extraction and matching. However, iris masks can also have a great impact on the accuracy of recognition. In this study, we propose two iris mask estimation algorithm based on deep learning. After pre-processing the iris images and the corresponding masks, we train these data in convolution neural networks (CNN), which help to achieve a higher accuracy in matching iris masks for different images than rule-based algorithms. The accuracy of matching by using patch-based CNN is 92.87%, with the 0.147% EER (Equal Error Rate) and the accuracy of applying multi-channel fully convolution networks is 95.56%, with an even lower EER equal to 0.0851%.

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Kumar, Rohit. "Mask Estimator Approaches For Audio Beamforming." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4711.

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Beamforming is a family of algorithms and performs a spatial filtering operation that makes it possible to map the distribution of the sources at a certain distance from the microphones and therefore locate the strongest source. The state-of-art methods for acoustic beamforming in multi-channel ASR are based on a neural mask estimator that predicts the presence of speech and noise, which in turn used to determine spatial filter coefficients value. These models are trained using a paired corpus of clean and noisy recordings (teacher model). In this thesis, we attempt to move away from the requirements of having supervised clean recordings for training the mask estimator. The models based on signal enhancement and beamforming using multi-channel linear prediction serve as the required mask estimate. In this way, the model training can also be carried out on real recordings of noisy speech rather than simulated ones alone done in a typical teacher model. We propose two model in this thesis, both based on Unsupervised Mask estimation, and several experiments performed on noisy and reverberant environments in the CHiME-3 corpus as well as the REVERB challenge corpus highlight the effectiveness of the proposed approaches. Both the method that we discuss are novel method, where the first model only deals with the real data, the second model deals with complex data i,e complex short time Fourier transform features to obtain the mask estimate.

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朱大衛. "Neural Networks Application in Estimating Strong Motion Characteristics at Main Lines of Kaohsiung Mass Rapid Transit." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/47520411226944330815.

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碩士
國立屏東科技大學
土木工程系碩士班
89
The effect of strong ground motion in a construction site is an important issue, which must be considered for a practical design in structural engineering. The actual records by seismometer at each station obtained from Central Weather Bureau (CWB) may be taken as a basic data, but a reliable estimation method may be useful for providing more detailed information of the strong motion characteristics related to the site. Therefore, the purpose of this study is by using back-propagation neural networks, and based on the inputs of epicentral distance, focal depth and magnitude, to develop a model for estimating peak ground acceleration in the major sections on Red and Orange lines of the Kaohsiung Mass Rapid Transit (KMRT). Two major parts are included in this study, at first, by input various parameters, a better estimation model is obtained from computational experiments, and compared to the available different nonlinear regression analysis to prove the ability of present neural network models. Secondly, the application to sections on the Red and Orange lines are estimated and compared with the results from microtremor measurements. From the comparisons, the results showed that the present neural network models have a better performance than the other methods, which may provide more reasonable results and closer to the actual records. Thus, the neural networks proved to be very useful in estimating the strong motion characteristics for a construction site, and may provide valuable inputs from theoretical and practical standpoints.

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Ha, QP. "On robustness of motion control systems." Thesis, 1996. https://eprints.utas.edu.au/17907/2/whole-thesis-ha.pdf.

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Robustness and intelligence are becoming increasingly important in motion control systems. In multi-mass electromechanical systems, the estimation of damping capability and design of robust controllers are among very important aspects. In this thesis, conventional control methods coupled with fuzzy logic and neural networks are used to address these issues. First, damping capability of multi-mass electromechanical systems is estimated. The maximal damping and complete damping cases are determined using the generalised model for a multi-mass electromechanical system. To eliminate the load variation influence and reduce elastic vibrations, robust modal control is proposed with observer-based state feedback and feedforward compensation. The use of fuzzy logic dealing with uncertainties is investigated. Good transient performance is obtained, even in the case of changing plant parameters, by fuzzy tuning of the proportional-integral (PI) controller parameters. It is shown that PI controllers with fuzzy tuning can be used in cascade control in a two-mass system. Fuzzy tuning schemes, based on expert knowledge, can be applied to sliding mode control to accelerate the reaching phase and reduce chattering for robustness enhancement. In robust modal control , taking into account uncertainties in the plant parameters and disturbance rate of change, an improvement of observer robustness is achieved via a fuzzy tuning scheme of the predictive coefficient. Insensitivity to load variations is enhanced by continuously tuning the feedforward compensation coefficients. These fuzzy tuning schemes can be applied to robust modal control of multi-mass systems in the presence of uncertainties. Since tuning is a continuous process, exponential membership functions are used. However, with Gaussian or sigmoidal membership functions, similar results can also be obtained. Observer robustness achieved by fuzzy tuning is demonstrated to be suitable for incipient fault detection in dynamic systems. Neural network-based techniques to the problem concerned are also presented. It is shown that a neural net controller can replace the role of a feedforward controller or a fuzzy logic controller. Moreover, a neural net-based controller can be used as a classifier for recognising the error and derivative-of-error patterns, and providing an appropriate control action to improve tracking performance. The proposed controller can be used in a two-mass system without a priori knowledge of the plant. Neuro-fuzzy approach is introduced with a feedforward compensation from an observer based control loop and robust enhancement from a neural network model. Tuning is a human experience to increase robustness. Fuzzy tuning is shown to be efficient thanks to the possibility of adopting this experience. Neurotuning with learning capability will be a subject for further research.

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Book chapters on the topic "Neural Mask Estimation"

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Hasimoto-Beltran, Rogelio, Odin F. Eufracio-Vazquez, and Berenice Calderon-Damian. "Deep Neural Networks for Passengers’ Density Estimation and Face Mask Detection for COVID-19 in Public Transportation Services." In Intelligent Systems Reference Library, 21–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06307-7_2.

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López-Espejo, Iván, José A. González, Ángel M. Gómez, and Antonio M. Peinado. "A Deep Neural Network Approach for Missing-Data Mask Estimation on Dual-Microphone Smartphones: Application to Noise-Robust Speech Recognition." In Advances in Speech and Language Technologies for Iberian Languages, 119–28. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13623-3_13.

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Zhang, Zhi Qiang, Qing Ming Wu, Qiang Zhang, and Zhi Chao Gong. "Estimation of Rock Mass Rating System with an Artificial Neural Network." In Advances in Neural Networks – ISNN 2009, 963–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01513-7_106.

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Rocha-Mancera, M. F., S. Arce-Benítez, L. Torres, and J. E. G. Vázquez. "Estimation of Mass Flow Rates of Two-Phase Flow Using Convolutional Neural Networks." In Intelligent and Safe Computer Systems in Control and Diagnostics, 190–201. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16159-9_16.

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Hari Priya K and Malathi S. "Efficient Face Mask Recognition System by Using Deep Learning Methodology." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210047.

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In this project, mask Recognition System is presented, that utilizes the prospect of Object Detection, completed the assistance of Deep Learning philosophies. The past work gives the topic of article identification by joining coarse-grained and fine-grained discovery philosophies without precedent for police work the moving items on high goal recordings. Period object location and acknowledgment finds careful applications in various fields like clinical applications, security police examination, and independent vehicles. There unit of estimation a few machine and profound learning procedures that unit utilized for object discovery and acknowledgment. The development of a convolutional neural organization (CNN) has given a major forward leap to protest discovery and acknowledgment. Convolutional Neural Network (CNN) has arrived at the exemplification of picture characterization for different application. Explicitly in 2D-CNN, there’s huge advancement for object discovery, beside 3D-CNN, it’s still toward the start of partner time. Profound CNN’s unit acclimated get extra exact directions and to deal with high goal video outlines. By taking this idea of location on the grounds that the base to our work, the framework includes in perceiving the essence of the individual and checks if the face have mask or not, befittingly guaranteeing the individual complies with the assurance safety measures so as that the unfurl of hepatotoxic infection may be managed. The framework will unpredictably be utilized for future face ID with face mask till the pandemic gets died.

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La Cruz, Alexandra, Erika Severeyn, Mónica Huerta, and Sara Wong. "Support Vector Machine Technique as Classifier of Impaired Body Fat Percentage." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210188.

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Excess weight and obesity are indicators of an unhealthy or harmful accumulation of fat that can be dangerous to health. Body mass index (BMI) refers to height-to-weight radio and is often used to identify overweight and obesity in adults. Although BMI is commonly used to diagnose obesity and overweight, it is ineffective in differentiating between high muscle mass and elevated body fat mass. Body fat percentage (BF%) is one of the best predictors of obesity because it quantifies adipose tissue. The Deurenberg equation is among the indirect methods to measure BF%; it uses BMI, age, and sex as parameters to calculate the BF%. Machine learning techniques demonstrated to be a good classifier of overweight, obesity, and diseases related to insulin resistance and metabolic syndrome. This study intends to evaluate anthropometric parameters as classifiers of BF% alteration using support vector machines and the Deurenberg equation for BF% estimation. The database used consisted of 1978 individuals with 24 different anthropometric measurements. The results suggest the SVM as a suitable technique for classifying individuals with normal and abnormal BF% values. Accuracy, F1 score, PPV, NPV, and sensitivity were above 0.8. Besides, the specificity value is below 0.7, which indicates that false positives may occur. As future work, this research intends to apply neural networks as a classification technique.

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"Neural Network Approach for Estimating Mass Moments of Inertia and Center of Gravity in Military Vehicles." In Intelligent Engineering Systems through Artificial Neural Networks, Volume 16, 817–22. ASME Press, 2006. http://dx.doi.org/10.1115/1.802566.paper121.

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Bhatia, Sheetal. "Brain-Computer Interface and Neurofeedback for Brain Training." In Interdisciplinary Approaches to Altering Neurodevelopmental Disorders, 200–212. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3069-6.ch012.

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AT liberty and open correspondence is central to present day life. Human cerebrum PC interfaces (BCIs), which interpret estimations of the client's mind movement into PC directions, present developing types of without hands correspondence. BCI correspondence frameworks have since quite a while ago been utilized in clinical settings for patients with loss of motion and other engine issue and have not been executed with the expectation of complimentary correspondence between solid, BCI-gullible clients. Brain PC interface innovation speaks to an exceptionally developing field of research with application frameworks. Its commitments in therapeutic fields extend from avoidance to neuronal restoration for genuine wounds. Human intellect perusing and remote correspondence have their exceptional unique mark in various fields, for example, instructive, self-guideline, creation, advertising, security, just as games and excitement. It makes a shared comprehension among clients and the encompassing frameworks.

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Ordóñez, Diego, Carlos Dafonte, Bernardino Arcay, and Minia Manteiga. "Connectionist Systems and Signal Processing Techniques Applied to the Parameterization of Stellar Spectra." In Soft Computing Methods for Practical Environment Solutions, 187–203. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch012.

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A stellar spectrum is the finger-print identification of a particular star, the result of the radiation transport through its atmosphere. The physical conditions in the stellar atmosphere, its effective temperature, surface gravity, and the presence and abundance of chemical elements explain the observed features in the stellar spectra, such as the shape of the overall continuum and the presence and strength of particular lines and bands. The derivation of the atmospheric stellar parameters from a representative sample of stellar spectra collected by ground-based and spatial telescopes is essential when a realistic view of the Galaxy and its components is to be obtained. In the last decade, extensive astronomical surveys recording information of large portions of the sky have become a reality since the development of robotic or semi-automated telescopes. The Gaia satellite is one of the key missions of the European Space Agency (ESA) and its launch is planned for 2011. Gaia will carry out the so-called Galaxy Census by extracting precise information on the nature of its main constituents, including the spectra of objects (Wilkinson, 2005). Traditional methods for the extraction of the fundamental atmospheric stellar parameters (effective temperature (Teff), gravity (log G), metallicity ([Fe/H]), and abundance of alpha elements [a/Fe], elements integer multiples of the mass of the helium nucleus) are time-consuming and unapproachable for a massive survey involving 1 billion objects (about 1% of the Galaxy constituents) such as Gaia. This work presents the results of the authors’ study and shows the feasibility of an automated extraction of the previously mentioned stellar atmospheric parameters from near infrared spectra in the wavelength region of the Gaia Radial Velocity Spectrograph (RVS). The authors’ approach is based on a technique that has already been applied to problems of the non-linear parameterization of signals: artificial neural networks. It breaks ground in the consideration of transformed domains (Fourier and Wavelet Transforms) during the preprocessing stage of the spectral signals in order to select the frequency resolution that is best suited for each atmospheric parameter. The authors have also progressed in estimating the noise (SNR) that blurs the signal on the basis of its power spectrum and the application of noise-dependant algorithms of parameterization. This study has provided additional information that allows them to progress in the development of hybrid systems devoted to the automated classification of stellar spectra.

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Conference papers on the topic "Neural Mask Estimation"

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Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. "Neural network based spectral mask estimation for acoustic beamforming." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471664.

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Zhang, Xi, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, and Gady Agam. "Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/163.

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Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow by combining a layered motion representation with deep learning. Instead of pre-segmenting the image to layers, the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module. The essential components of the soft-mask module are maxout and fuse operations, which enable a disjoint layered representation of optical flow and more accurate flow estimation. We show that by using masks the motion estimate results in a quadratic function of input features in the output layer. The proposed soft-mask module can be added to any existing optical flow estimation networks by replacing their flow output layer. In this work, we use FlowNet as the base network to which we add the soft-mask module. The resulting network is tested on three well-known benchmarks with both supervised and unsupervised flow estimation tasks. Evaluation results show that the proposed network achieve better results compared with the original FlowNet.

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Narayanan, Arun, and DeLiang Wang. "Ideal ratio mask estimation using deep neural networks for robust speech recognition." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639038.

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Li, Kai, Xiaolin Hu, and Yi Luo. "On the Use of Deep Mask Estimation Module for Neural Source Separation Systems." In Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-174.

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Zheng, W. Q., Y. X. Zou, and C. Ritz. "Spectral mask estimation using deep neural networks for inter-sensor data ratio model based robust DOA estimation." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7177984.

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Tu, Yan-Hui, Jun Du, and Chin-Hui Lee. "2D-to-2D Mask Estimation for Speech Enhancement Based on Fully Convolutional Neural Network." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054615.

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Li, Xu, Junfeng Li, and Yonghong Yan. "Ideal Ratio Mask Estimation Using Deep Neural Networks for Monaural Speech Segregation in Noisy Reverberant Conditions." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-549.

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Zhou, Ying, and Yanmin Qian. "Robust Mask Estimation By Integrating Neural Network-Based and Clustering-Based Approaches for Adaptive Acoustic Beamforming." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462462.

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Bednarek, Daniel R., Stephen Rudin, and Swetadri Vasan Setlur Nagesh. "Estimation of attenuator mask from region of interest (ROI) dose-reduced images for brightness equalization using convolutional neural networks." In Physics of Medical Imaging, edited by Hilde Bosmans, Guang-Hong Chen, and Taly Gilat Schmidt. SPIE, 2019. http://dx.doi.org/10.1117/12.2512646.

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Hadjahmadi, Amir Hossein, Mohammad Mehdi Homayounpour, and Seyed Mohammad Ahadi. "A Neural Network based local SNR estimation for estimating spectral masks." In 2008 International Symposium on Telecommunications (IST). IEEE, 2008. http://dx.doi.org/10.1109/istel.2008.4651373.

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Reports on the topic "Neural Mask Estimation"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.

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