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Статті в журналах з теми "Raw waveform"
Stelling, N., and K. Richter. "VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 755–62. http://dx.doi.org/10.5194/isprs-archives-xli-b8-755-2016.
Повний текст джерелаStelling, N., and K. Richter. "VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 755–62. http://dx.doi.org/10.5194/isprsarchives-xli-b8-755-2016.
Повний текст джерелаXu, D. C., B. D. Xu, E. J. Bao, Y. Y. Wu, A. Q. Zhang, Y. Y. Wang, G. L. Zhang, et al. "Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments." Journal of Instrumentation 17, no. 06 (June 1, 2022): P06040. http://dx.doi.org/10.1088/1748-0221/17/06/p06040.
Повний текст джерелаLi, Shaobo, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, and Jianjun Hu. "An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition." Applied Sciences 8, no. 7 (July 15, 2018): 1152. http://dx.doi.org/10.3390/app8071152.
Повний текст джерелаYounis, Raneen, and Andreas Reinhardt. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures." Energies 13, no. 12 (June 12, 2020): 3039. http://dx.doi.org/10.3390/en13123039.
Повний текст джерелаLi, Zheming, and Wei He. "A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model." Sensors 21, no. 21 (October 29, 2021): 7207. http://dx.doi.org/10.3390/s21217207.
Повний текст джерелаPashaei, Mohammad, Michael J. Starek, Craig L. Glennie, and Jacob Berryhill. "Terrestrial Lidar Data Classification Based on Raw Waveform Samples Versus Online Waveform Attributes." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–19. http://dx.doi.org/10.1109/tgrs.2021.3132356.
Повний текст джерелаMoriya, Hirokazu. "Phase-only correlation of time-varying spectral representations of microseismic data for identification of similar seismic events." GEOPHYSICS 76, no. 6 (November 2011): WC37—WC45. http://dx.doi.org/10.1190/geo2011-0021.1.
Повний текст джерелаCole, P. T., and M. Carlos. "Use of Advanced A.E. Analysis for Source Discrimination Using Captured Waveforms." Advanced Materials Research 13-14 (February 2006): 401–6. http://dx.doi.org/10.4028/www.scientific.net/amr.13-14.401.
Повний текст джерелаZhang, Yu, Bei Wang, Jin Jing, Jian Zhang, Junzhong Zou, and Masatoshi Nakamura. "A Comparison Study on Multidomain EEG Features for Sleep Stage Classification." Computational Intelligence and Neuroscience 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4574079.
Повний текст джерелаДисертації з теми "Raw waveform"
Zeghidour, Neil. "Learning representations of speech from the raw waveform." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE004/document.
Повний текст джерелаWhile deep neural networks are now used in almost every component of a speech recognition system, from acoustic to language modeling, the input to such systems are still fixed, handcrafted, spectral features such as mel-filterbanks. This contrasts with computer vision, in which a deep neural network is now trained on raw pixels. Mel-filterbanks contain valuable and documented prior knowledge from human auditory perception as well as signal processing, and are the input to state-of-the-art speech recognition systems that are now on par with human performance in certain conditions. However, mel-filterbanks, as any fixed representation, are inherently limited by the fact that they are not fine-tuned for the task at hand. We hypothesize that learning the low-level representation of speech with the rest of the model, rather than using fixed features, could push the state-of-the art even further. We first explore a weakly-supervised setting and show that a single neural network can learn to separate phonetic information and speaker identity from mel-filterbanks or the raw waveform, and that these representations are robust across languages. Moreover, learning from the raw waveform provides significantly better speaker embeddings than learning from mel-filterbanks. These encouraging results lead us to develop a learnable alternative to mel-filterbanks, that can be directly used in replacement of these features. In the second part of this thesis we introduce Time-Domain filterbanks, a lightweight neural network that takes the waveform as input, can be initialized as an approximation of mel-filterbanks, and then learned with the rest of the neural architecture. Across extensive and systematic experiments, we show that Time-Domain filterbanks consistently outperform melfilterbanks and can be integrated into a new state-of-the-art speech recognition system, trained directly from the raw audio signal. Fixed speech features being also used for non-linguistic classification tasks for which they are even less optimal, we perform dysarthria detection from the waveform with Time-Domain filterbanks and show that it significantly improves over mel-filterbanks or low-level descriptors. Finally, we discuss how our contributions fall within a broader shift towards fully learnable audio understanding systems
Erkmen, Baris I., Andre Tkacenko, and Clayton M. Okino. "Preamble Design for Symbol Timing Estimation from SOQPSK-TG Waveforms." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606119.
Повний текст джерелаData-aided symbol synchronization for bursty communications utilizes a predetermined modulation sequence, i.e., a preamble, preceding the payload. For effective symbol synchronization, this preamble must be designed in accordance with the modulation format. In this paper, we analyze preambles for shaped offset quadrature phase-shift keying (SOQPSK) waveforms. We compare the performance of several preambles by deriving the Cram´er-Rao bound (CRB), and identify a desirable one for the Telemetry Group variant of SOQPSK. We also demonstrate, via simulation, that the maximum likelihood estimator with this preamble approaches the CRB at moderate signal-to-noise ratio.
Vemula, Hari Charan. "Multiple Drone Detection and Acoustic Scene Classification with Deep Learning." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547384408540764.
Повний текст джерелаRistorcelli, Thomas. "Evaluation de l'apport des visées multi-angulaires en imagerie laser pour la reconstruction 3D des couverts végétaux." Thesis, Toulouse, ISAE, 2013. http://www.theses.fr/2013ESAE0049/document.
Повний текст джерелаThis research work regards the scientific challenge of reconstructing the ground and the object presents under a vegetation cover from airborne observations. Airborne laser scanning is a promising technology. Full-waveform devices are able to record the complete temporal return signal following the emission of a short laser pulse towards the ground. This offers a great potential for remote sensing of forested areas, since the laser pulse will travel through the vegetation. Many commercial systems are already operated for topography or bathymetry. Scientists have been using these systems for vegetation observation, even if they are not dedicated to this purpose. The objective of this thesis is to study the relevance of full-waveform lidars for the geometric reconstruction of digital terrain models (DTM) under vegetation. We also aim at developing simulation and data processing toolsthat will help design and optimize future sensors dedicated to vegetation observation. Our first task was the development of a new physical simulator for full-waveform lidar measurement. The DELiS model (n-Dimensional Estimation of Lidar Signals) is able tosimulate the observation of complex and realistic vegetation scenes while accounting for atmosphere and sun perturbations, and simulating the multiple scattering of the laser pulse in the canopy. We have also implemented a sensor model for simulation of the measurement, amplification and digitization noises. This operational simulation tool is a key asset for future physical studies as well as for designing and optimizing future sensors and data processing methods. After validating the DELiS model by confrontation with analytical results, we have used it for studying the interest of full-waveform lidar for digital terrain models reconstruction under vegetation. For this purpose, we have developed a full-waveform lidar data processing method for decomposition of the signals and classification of the lidar echoes into two classes : ’ground’ and ’vegetation’. We were then able to reconstruct ground geometry.Finally, we have led a study on the combination of multi-angular acquisitions for improvement of the reconstructions.Our work shows that airborne full-waveform lidar observations may allow ground reconstruction with sub-metric resolutions and a precision of 10 to 20 centimeters in forested areas. Combining multiple viewing angles provides additional data, and helps improving the precision of the reconstructions. Yet, we show that non-nadir viewing is much more sensitive to trunks and branches. These elements may be the cause of an additional error in the classification and reconstruction processes. For this reason, we recommend using nadir viewing for single-view ground reconstruction, and propose a method for optimally selecting non-nadir views for the detailed observation of restricted areas of interest
Menni, Tarek. "Borne de Cramér-Rao déterministe pour l'analyse des performances asymptotiques en estimation d'un radar actif." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00846933.
Повний текст джерелаFernández, Vicente Juan. "Reconfigurable Reflective Arrayed Waveguide Grating on Silicon Nitride." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/165783.
Повний текст джерела[CAT] La present tesi s'ha centrat en el modelatge, disseny i demonstració experimental per primera vegada del dispositiu Reconfigurable Reflective Arrayed Waveguide Grating (R-RAWG). Per a la consecució d'aquest dispositiu que té possibilitats d'ús en l'espectrometria, una plataforma de nitrur de silici anomenada CNM-VLC s'ha usat ja que aquest material permet operar en una gran amplada de banda. Aquesta plataforma posseeix certes limitacions i els elements necessaris per al funcionament d'aquest dispositiu tenien un performance baix. Per això, s'ha desenvolupat i validat una metodologia que ha permés obtindre millors divisors i també, gràcies als processos de fabricació, s'ha dissenyat un acoplador que ha millorat considerablement l'acoble de llum al xip. Això ha sigut gràcies a un exhaustiu analisis d'opcions existents en la literatura que també ha permés triar la millor opció per a realitzar un espill reconfigurable en la plataforma sense canviar ni afegir cap procés de fabricació. S'han demonstrat espills reconfigurables gràcies a utilitzar divisors realimentats i també s'ha desenvolupat codis que prediuen el comportament del dispostiu experimentalment. Amb tot el treball realitzat, s'ha dissenyat un R-RAWG fent ús de determinades consideracions perquè poguera operar en una gran amplada de banda i que els actuadors de fase no tingueren perill de desbaratar-se. També s'ha desenvolupat un codi per al modelatge del R-RAWG que permet imitar la fabricació d'aquests dispositius i que, gràcies a això, s'ha desenvolupat un mètode o algorisme anomenat DPASTOR, que usa algorismes usats en machine learning, per a optimitzar la resposta amb tan sols la potència òptica d'eixida. Finalment, s'ha dissenyat una PCB per a poder connectar elèctricament el xip fotònic i s'ha desenvolupat un mètode de mesura que ha permés tindre una resposta estable aconseguint demostrar multitud de respostes de filtres òptics amb el mateix dispositiu.
[EN] This thesis is focused on the modelling, design and experimental demonstration for the first time of Reconfigurable Reflective Arrayed Waveguide Grating (R-RAWG) device. In order to build this device, that can be employed in spectrometry, a silicon nitride platform termed CNM-VLC has been chosen since this material allows to operate in broad range of wavelengths. This platform has the necessary elements, but some limitations because the operation of this device had a low performance. Therefore, a methodology has been developed and validated, which has allowed to obtain better splitters. Also an inverted taper has been designed, which has considerably improved the coupling of light to the chip. This has been possible thanks to an exhaustive analysis of existing options in the literature, that has allowed choosing the best option to make a reconfigurable mirror on the platform without changing or adding new manufacturing steps. Reconfigurable mirrors have been demonstrated by using feedback splitters. Furthermore, codes have been developed to predict the behaviour of the actual device. With all the work done, a R-RAWG has been designed by using certain considerations so that it can operate over a broad wavelength range and the phase actuators are not in danger of being damaged. A code has also been developed for the modelling of the R-RAWG, which allows manufacturing imperfections to be considered, thanks to this, a method or algorithm called DPASTOR has been developed. DPASTOR resembles machine learning to optimise the response by just using the optical output power. Finally, a PCB and an assembly with the chip interconnected to it have been made and designed. Moreover, a measurement method has been developed, which has made it possible to have a stable response and to demonstrate a multitude of optical filter responses with the same device.
Fernández Vicente, J. (2021). Reconfigurable Reflective Arrayed Waveguide Grating on Silicon Nitride [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/165783
TESIS
Huang, Tzu-Yun, and 黃姿云. "A Dual Complementary Acoustic Embedding Network: Mining Discriminative Characteristics from Raw-waveform for Speech Emotion Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y8zcm7.
Повний текст джерелаAgrawal, Purvi. "Neural Representation Learning for Speech and Audio Signals." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4824.
Повний текст джерелаHsieh, Yu-Shih, and 謝雨蒔. "The Analysis and Comparison of the Action Potential Waveforms in Ventral Posterolateral Nucleus of the Rat Thalamus." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/43504880861748979191.
Повний текст джерела國立臺灣大學
動物學研究研究所
94
The ventral posterolateral thalamic nucleus ( VPL ) plays an important role in conveying the somatosensory signals. It contains different sorts of neurons which may generate diverse action potential waveforms, their reactions to innocuous or noxious stimuli also diverge. This study investigates the classification of action potential waveforms in VPL, the relationship between different recording microelectrodes or functional groups and waveforms were further compared. The result shows that VPL waveforms could be divided into four main groups by the features such as the peak-to-peak amplitude, the peak-to-peak duration, etc. The materials of recording microelectrodes affect the kinds of waveforms being collected. However, the different functional groups of VPL neurons do not correspond to particular kinds of waveforms.
Bouse, Scott. "Investigation of Transfer Function Analysis as a Means to Predict Strain on Rat Tibiae from Ankle Torque Waveforms." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7226.
Повний текст джерелаКниги з теми "Raw waveform"
Electronics via waveform analysis. New York: Springer-Verlag, 1993.
Знайти повний текст джерелаMcAlpine, Kenneth B. The Ultimate Soundtracker? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190496098.003.0006.
Повний текст джерелаЧастини книг з теми "Raw waveform"
Li, Wencheng, Zhenhua Tan, Zhenche Xia, Danke Wu, and Jingyu Ning. "PF-Net: Personalized Filter for Speaker Recognition from Raw Waveform." In Mobile Multimedia Communications, 362–74. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23902-1_28.
Повний текст джерелаSarma, Mousmita, Kandarpa Kumar Sarma, and Nagendra Kumar Goel. "Children’s Age and Gender Recognition from Raw Speech Waveform Using DNN." In Lecture Notes in Networks and Systems, 1–9. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2774-6_1.
Повний текст джерелаMaset, Eleonora, Roberto Carniel, and Fabio Crosilla. "Unsupervised Classification of Raw Full-Waveform Airborne Lidar Data by Self Organizing Maps." In Image Analysis and Processing — ICIAP 2015, 62–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_6.
Повний текст джерелаMadariaga, Raúl. "Waveform Synthesis by Ray Theoretical Methods." In Digital Seismology and Fine Modeling of the Lithosphere, 49–78. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-6759-6_4.
Повний текст джерелаDonnelly, Patrick J., and Parker Carlson. "Transposition of Simple Waveforms from Raw Audio with Deep Learning." In Artificial Intelligence in Music, Sound, Art and Design, 341–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29956-8_22.
Повний текст джерелаOshio, Kotaro, Hidetaka Onodera, Masashi Uchida, Yuichiro Tanaka, and Takuo Hashimoto. "Assessment of Brain Compliance Using ICP Waveform Analysis in Water Intoxication Rat Model." In Brain Edema XV, 219–21. Vienna: Springer Vienna, 2013. http://dx.doi.org/10.1007/978-3-7091-1434-6_41.
Повний текст джерелаSumma, D. A., T. N. Claytor, M. H. Jones, M. J. Schwab, and S. C. Hoyt. "3-D Visualization of X-Ray and Neutron Computed Tomography (CT) and Full Waveform Ultrasonic (UT) Data." In Review of Progress in Quantitative Nondestructive Evaluation, 927–34. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4791-4_119.
Повний текст джерелаKristek, Frantisek, Marian Grman, and Karol Ondrias. "In Vivo Measurement of H2S, Polysulfides, and “SSNO− Mix”-Mediated Vasoactive Responses and Evaluation of Ten Hemodynamic Parameters from Rat Arterial Pulse Waveform." In Methods in Molecular Biology, 109–24. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9528-8_8.
Повний текст джерелаRhode, Kawal, Gareth Ennew, Tryphon Lambrou, Alexander Seifalian, and David Hawkes. "In-Vitro Validation of a Novel Model-Based Approach to the Measurement of Arterial Blood Flow Waveforms from Dynamic Digital X-ray Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, 291–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_35.
Повний текст джерелаBansal, Dipali. "Emerging Technologies and ICT Solutions in Healthcare." In Handbook of Research on Healthcare Administration and Management, 268–86. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0920-2.ch016.
Повний текст джерелаТези доповідей конференцій з теми "Raw waveform"
Pashaei, Mohammad, Michael J. Starek, and Jacob Berryhill. "Full-Waveform Terrestrial Lidar Data Classification Using Raw Digitized Waveform Signals." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883782.
Повний текст джерелаMuckenhirn, Hannah, Vinayak Abrol, Mathew Magimai-Doss, and Sébastien Marcel. "Understanding and Visualizing Raw Waveform-Based CNNs." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2341.
Повний текст джерелаAgrawal, Purvi, and Sriram Ganapathy. "Unsupervised Raw Waveform Representation Learning for ASR." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2652.
Повний текст джерелаRavanelli, Mirco, and Yoshua Bengio. "Speaker Recognition from Raw Waveform with SincNet." In 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018. http://dx.doi.org/10.1109/slt.2018.8639585.
Повний текст джерелаPashaei, Mohammad, Michael J. Starek, Philippe Tissot, and Jacob Berryhill. "Full-Waveform Terrestrial Lidar Data Classification Using Raw Samples of Digitized Waveform." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553327.
Повний текст джерелаSainath, Tara N., Ron J. Weiss, Kevin W. Wilson, Arun Narayanan, and Michiel Bacchiani. "Factored spatial and spectral multichannel raw waveform CLDNNs." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472644.
Повний текст джерелаKethireddy, Rashmi, Sudarsana Reddy Kadiri, and Suryakanth V. Gangashetty. "Learning Filterbanks from Raw Waveform for Accent Classification." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206778.
Повний текст джерелаPlaten, P. von, Chao Zhang, and P. C. Woodland. "Multi-Span Acoustic Modelling Using Raw Waveform Signals." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2454.
Повний текст джерелаJuvela, Lauri, Vassilis Tsiaras, Bajibabu Bollepalli, Manu Airaksinen, Junichi Yamagishi, and Paavo Alku. "Speaker-independent Raw Waveform Model for Glottal Excitation." In Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1635.
Повний текст джерелаJung, Jee-weon, Youjin Kim, Hee-Soo Heo, Bong-Jin Lee, Youngki Kwon, and Joon Son Chung. "Pushing the limits of raw waveform speaker recognition." In Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-126.
Повний текст джерелаЗвіти організацій з теми "Raw waveform"
Nishimura, K., B. Dey, D. Aston, D. W. G. S. Leith, B. Ratcliff, D. Roberts, L. Ruckman, D. Shtol, G. S. Varner, and J. Va’vra. A Detailed Study of FDIRC Prototype with Waveform Digitizing Electronics in Cosmic Ray Telescope Using 3D Tracks. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1055456.
Повний текст джерела