Literatura académica sobre el tema "Neural Mask Estimation"
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Artículos de revistas sobre el tema "Neural Mask Estimation"
Om, Chol Nam, Hyok Kwak, Chong Il Kwak, Song Gum Ho y 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, n.º 01 (2023): 5754–59. http://dx.doi.org/10.35444/ijana.2023.15101.
Texto completoLee, Hyo-Jun, Jong-Hyeon Baek, Young-Kuk Kim, Jun Heon Lee, Myungjae Lee, Wooju Park, Seung Hwan Lee y Yeong Jun Koh. "BTENet: Back-Fat Thickness Estimation Network for Automated Grading of the Korean Commercial Pig". Electronics 11, n.º 9 (19 de abril de 2022): 1296. http://dx.doi.org/10.3390/electronics11091296.
Texto completoBezsonov, Oleksandr, Oleh Lebediev, Valentyn Lebediev, Yuriy Megel, Dmytro Prochukhan y 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, n.º 9 (114) (29 de diciembre de 2021): 64–74. http://dx.doi.org/10.15587/1729-4061.2021.247648.
Texto completoLee, Chang-bok, Han-sung Lee y Hyun-chong Cho. "Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images". Applied Sciences 13, n.º 5 (23 de febrero de 2023): 2896. http://dx.doi.org/10.3390/app13052896.
Texto completoGuimarães, André, Maria Valério, Beatriz Fidalgo, Raúl Salas-Gonzalez, Carlos Pereira y Mateus Mendes. "Cork Oak Production Estimation Using a Mask R-CNN". Energies 15, n.º 24 (17 de diciembre de 2022): 9593. http://dx.doi.org/10.3390/en15249593.
Texto completoHasannezhad, Mojtaba, Zhiheng Ouyang, Wei-Ping Zhu y 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.
Texto completoSivapatham, Shoba, Asutosh Kar, Roshan Bodile, Vladimir Mladenovic y Pitikhate Sooraksa. "A deep neural network-correlation phase sensitive mask based estimation to improve speech intelligibility". Applied Acoustics 212 (septiembre de 2023): 109592. http://dx.doi.org/10.1016/j.apacoust.2023.109592.
Texto completoOsorio, Kavir, Andrés Puerto, Cesar Pedraza, David Jamaica y Leonardo Rodríguez. "A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images". AgriEngineering 2, n.º 3 (28 de agosto de 2020): 471–88. http://dx.doi.org/10.3390/agriengineering2030032.
Texto completoSong, Junho, Yonggu Lee y Euiseok Hwang. "Time–Frequency Mask Estimation Based on Deep Neural Network for Flexible Load Disaggregation in Buildings". IEEE Transactions on Smart Grid 12, n.º 4 (julio de 2021): 3242–51. http://dx.doi.org/10.1109/tsg.2021.3066547.
Texto completoRizwan, Tahir, Yunze Cai, Muhammad Ahsan, Noman Sohail, Emad Abouel Nasr y 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.
Texto completoTesis sobre el tema "Neural Mask Estimation"
Chen, Jitong. "On Generalization of Supervised Speech Separation". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492038295603502.
Texto completoNarayanan, Arun. "Computational auditory scene analysis and robust automatic speech recognition". The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1401460288.
Texto completoGarnier, 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.
Texto completoA 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
Li, Meng-Huan y 李孟桓. "Robust and Accurate Iris Mask Estimation using Convolutional Neural Network". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/81465880036765346893.
Texto completo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%.
Kumar, Rohit. "Mask Estimator Approaches For Audio Beamforming". Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4711.
Texto completo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.
Texto completo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.
Ha, QP. "On robustness of motion control systems". Thesis, 1996. https://eprints.utas.edu.au/17907/2/whole-thesis-ha.pdf.
Texto completoCapítulos de libros sobre el tema "Neural Mask Estimation"
Hasimoto-Beltran, Rogelio, Odin F. Eufracio-Vazquez y Berenice Calderon-Damian. "Deep Neural Networks for Passengers’ Density Estimation and Face Mask Detection for COVID-19 in Public Transportation Services". En Intelligent Systems Reference Library, 21–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06307-7_2.
Texto completoLópez-Espejo, Iván, José A. González, Ángel M. Gómez y Antonio M. Peinado. "A Deep Neural Network Approach for Missing-Data Mask Estimation on Dual-Microphone Smartphones: Application to Noise-Robust Speech Recognition". En 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.
Texto completoZhang, Zhi Qiang, Qing Ming Wu, Qiang Zhang y Zhi Chao Gong. "Estimation of Rock Mass Rating System with an Artificial Neural Network". En 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.
Texto completoRocha-Mancera, M. F., S. Arce-Benítez, L. Torres y J. E. G. Vázquez. "Estimation of Mass Flow Rates of Two-Phase Flow Using Convolutional Neural Networks". En 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.
Texto completoHari Priya K y Malathi S. "Efficient Face Mask Recognition System by Using Deep Learning Methodology". En Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210047.
Texto completoLa Cruz, Alexandra, Erika Severeyn, Mónica Huerta y Sara Wong. "Support Vector Machine Technique as Classifier of Impaired Body Fat Percentage". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210188.
Texto completo"Neural Network Approach for Estimating Mass Moments of Inertia and Center of Gravity in Military Vehicles". En Intelligent Engineering Systems through Artificial Neural Networks, Volume 16, 817–22. ASME Press, 2006. http://dx.doi.org/10.1115/1.802566.paper121.
Texto completoBhatia, Sheetal. "Brain-Computer Interface and Neurofeedback for Brain Training". En Interdisciplinary Approaches to Altering Neurodevelopmental Disorders, 200–212. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3069-6.ch012.
Texto completoOrdóñez, Diego, Carlos Dafonte, Bernardino Arcay y Minia Manteiga. "Connectionist Systems and Signal Processing Techniques Applied to the Parameterization of Stellar Spectra". En Soft Computing Methods for Practical Environment Solutions, 187–203. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch012.
Texto completoActas de conferencias sobre el tema "Neural Mask Estimation"
Heymann, Jahn, Lukas Drude y Reinhold Haeb-Umbach. "Neural network based spectral mask estimation for acoustic beamforming". En 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471664.
Texto completoZhang, Xi, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan y Gady Agam. "Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask". En 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.
Texto completoNarayanan, Arun y DeLiang Wang. "Ideal ratio mask estimation using deep neural networks for robust speech recognition". En ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639038.
Texto completoLi, Kai, Xiaolin Hu y Yi Luo. "On the Use of Deep Mask Estimation Module for Neural Source Separation Systems". En Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-174.
Texto completoZheng, W. Q., Y. X. Zou y C. Ritz. "Spectral mask estimation using deep neural networks for inter-sensor data ratio model based robust DOA estimation". En ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7177984.
Texto completoTu, Yan-Hui, Jun Du y Chin-Hui Lee. "2D-to-2D Mask Estimation for Speech Enhancement Based on Fully Convolutional Neural Network". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054615.
Texto completoLi, Xu, Junfeng Li y Yonghong Yan. "Ideal Ratio Mask Estimation Using Deep Neural Networks for Monaural Speech Segregation in Noisy Reverberant Conditions". En Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-549.
Texto completoZhou, Ying y Yanmin Qian. "Robust Mask Estimation By Integrating Neural Network-Based and Clustering-Based Approaches for Adaptive Acoustic Beamforming". En ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462462.
Texto completoBednarek, Daniel R., Stephen Rudin y Swetadri Vasan Setlur Nagesh. "Estimation of attenuator mask from region of interest (ROI) dose-reduced images for brightness equalization using convolutional neural networks". En Physics of Medical Imaging, editado por Hilde Bosmans, Guang-Hong Chen y Taly Gilat Schmidt. SPIE, 2019. http://dx.doi.org/10.1117/12.2512646.
Texto completoHadjahmadi, Amir Hossein, Mohammad Mehdi Homayounpour y Seyed Mohammad Ahadi. "A Neural Network based local SNR estimation for estimating spectral masks". En 2008 International Symposium on Telecommunications (IST). IEEE, 2008. http://dx.doi.org/10.1109/istel.2008.4651373.
Texto completoInformes sobre el tema "Neural Mask Estimation"
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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