Littérature scientifique sur le sujet « Emotional filtering »
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Articles de revues sur le sujet "Emotional filtering"
Kim, Tae-Yeun, Hoon Ko, Sung-Hwan Kim et Ho-Da Kim. « Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering ». Sensors 21, no 6 (12 mars 2021) : 1997. http://dx.doi.org/10.3390/s21061997.
Texte intégralJenkins, Jeffrey. « Detecting emotional ambiguity in text ». MOJ Applied Bionics and Biomechanics 4, no 3 (25 mai 2020) : 55–57. http://dx.doi.org/10.15406/mojabb.2020.04.00134.
Texte intégralRULE, R. R., A. P. SHIMAMURA et R. T. KNIGHT. « Orbitofrontal cortex and dynamic filtering of emotional stimuli ». Cognitive, Affective, & ; Behavioral Neuroscience 2, no 3 (1 septembre 2002) : 264–70. http://dx.doi.org/10.3758/cabn.2.3.264.
Texte intégralGuerzoni, Michael A. « Vicarious trauma and emotional labour in researching child sexual abuse and child protection : A postdoctoral reflection ». Methodological Innovations 13, no 2 (mai 2020) : 205979912092634. http://dx.doi.org/10.1177/2059799120926342.
Texte intégralNosshi, Anthony, Aziza Saad Asem et Mohammed Badr Senousy. « Hybrid Recommender System Using Emotional Fingerprints Model ». International Journal of Information Retrieval Research 9, no 3 (juillet 2019) : 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.
Texte intégralSantamaria-Granados, Luz, Juan Francisco Mendoza-Moreno, Angela Chantre-Astaiza, Mario Munoz-Organero et Gustavo Ramirez-Gonzalez. « Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data ». Sensors 21, no 23 (25 novembre 2021) : 7854. http://dx.doi.org/10.3390/s21237854.
Texte intégralPrete, Giulia, Bruno Laeng et Luca Tommasi. « Modulating adaptation to emotional faces by spatial frequency filtering ». Psychological Research 82, no 2 (26 novembre 2016) : 310–23. http://dx.doi.org/10.1007/s00426-016-0830-x.
Texte intégralWang, Shu, Chonghuan Xu, Austin Shijun Ding et Zhongyun Tang. « A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network ». Electronics 10, no 15 (24 juillet 2021) : 1769. http://dx.doi.org/10.3390/electronics10151769.
Texte intégralMičieta, Branislav, Vladimíra Biňasová, Beáta Furmannová, Gabriela Gabajová et Marta Kasajová. « Emotional intelligence as an aspect in the performance of the work of a global manager ». SHS Web of Conferences 129 (2021) : 12002. http://dx.doi.org/10.1051/shsconf/202112912002.
Texte intégralKadiri, Sudarsana Reddy, et B. Yegnanarayana. « Epoch extraction from emotional speech using single frequency filtering approach ». Speech Communication 86 (février 2017) : 52–63. http://dx.doi.org/10.1016/j.specom.2016.11.005.
Texte intégralThèses sur le sujet "Emotional filtering"
Gobl, Christer. « The Voice Source in Speech Communication - Production and Perception Experiments Involving Inverse Filtering and Synthesis ». Doctoral thesis, KTH, Speech Transmission and Music Acoustics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3665.
Texte intégralThis thesis explores, through a number of production andperception studies, the nature of the voice source signal andhow it varies in spoken communication. Research is alsopresented that deals with the techniques and methodologies foranalysing and synthesising the voice source. The main analytictechnique involves interactive inverse filtering for obtainingthe source signal, which is then parameterised to permit thequantification of source characteristics. The parameterisationis carried by means of model matching, using the four-parameterLF model of differentiated glottal flow.
The first three analytic studies focus on segmental andsuprasegmental determinants of source variation. As part of theprosodic variation of utterances, focal stress shows for theglottal excitation an enhancement between the stressed voweland the surrounding consonants. At a segmental level, the voicesource characteristics of a vowel show potentially majordifferences as a function of the voiced/voiceless nature of anadjacent stop. Cross-language differences in the extent anddirectionality of the observed effects suggest differentunderlying control strategies in terms of the timing of thelaryngeal and supralaryngeal gestures, as well as in thelaryngeal tensions settings. Different classes of voicedconsonants also show differences in source characteristics:here the differences are likely to be passive consequences ofthe aerodynamic conditions that are inherent to the consonants.Two further analytic studies present voice source correlatesfor six different voice qualities as defined by Laver'sclassification system. Data from stressed and unstressedcontexts clearly show that the transformation from one voicequality to another does not simply involve global changes ofthe source parameters. As well as providing insights into theseaspects of speech production, the analytic studies providequantitative measures useful in technology applications,particularly in speech synthesis.
The perceptual experiments use the LF source implementationin the KLSYN88 synthesiser to test some of the analytic resultsand to harness them to explore the paralinguistic dimension ofspeech communication. A study of the perceptual salience ofdifferent parameters associated with breathy voice indicatesthat the source spectral slope is critically important andthat, surprisingly, aspiration noise contributes relativelylittle. Further perceptual tests using stimuli with differentvoice qualities explore the mapping between voice quality andits paralinguistic function of expressing emotion, mood andattitude. The results of these studies highlight the crucialrole of voice quality in expressing affect as well as providingpointers to how it combines withf0for this purpose.
The last section of the thesis focuses on the techniquesused for the analysis and synthesis of the source. Asemi-automatic method for inverse filtering is presented, whichis novel in that it optimises the inverse filter by exploitingthe knowledge that is typically used by the experimenter whencarrying out manual interactive inverse filtering. A furtherstudy looks at the properties of the modified LF model in theKLSYN88 synthesiser: it highlights how it differs from thestandard LF model and discusses the implications forsynthesising the glottal source signal from LF model data.Effective and robust source parameterisation for the analysisof voice quality is the topic of the final paper: theeffectiveness of global, amplitude-based, source parameters isexamined across speech tokens with large differences inf0. Additional amplitude-based parameters areproposed to enable a more detailed characterisation of theglottal pulse.
Keywords:Voice source dynamics, glottal sourceparameters, source-filter interaction, voice quality,phonation, perception, affect, emotion, mood, attitude,paralinguistic, inverse filtering, knowledge-based, formantsynthesis, LF model, fundamental frequency,f0.
Nallamilli, Sai Chandra Sekhar Reddy, et Nihanth Kandi. « Detection of Human Emotion from Noise Speech ». Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19610.
Texte intégralHoudek, Miroslav. « Rozpoznání emočního stavu člověka z řeči ». Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218117.
Texte intégralPatacca, Alessia. « The impact of emotional stressors on distractor filtering ». Doctoral thesis, 2019. http://hdl.handle.net/11562/995343.
Texte intégralDahmane, Mohamed. « Analyse de mouvements faciaux à partir d'images vidéo ». Thèse, 2011. http://hdl.handle.net/1866/7120.
Texte intégralIn a face-to-face talk, language is supported by nonverbal communication, which plays a central role in human social behavior by adding cues to the meaning of speech, providing feedback, and managing synchronization. Information about the emotional state of a person is usually carried out by facial attributes. In fact, 55% of a message is communicated by facial expressions whereas only 7% is due to linguistic language and 38% to paralanguage. However, there are currently no established instruments to measure such behavior. The computer vision community is therefore interested in the development of automated techniques for prototypic facial expression analysis, for human computer interaction applications, meeting video analysis, security and clinical applications. For gathering observable cues, we try to design, in this research, a framework that can build a relatively comprehensive source of visual information, which will be able to distinguish the facial deformations, thus allowing to point out the presence or absence of a particular facial action. A detailed review of identified techniques led us to explore two different approaches. The first approach involves appearance modeling, in which we use the gradient orientations to generate a dense representation of facial attributes. Besides the facial representation problem, the main difficulty of a system, which is intended to be general, is the implementation of a generic model independent of individual identity, face geometry and size. We therefore introduce a concept of prototypic referential mapping through a SIFT-flow registration that demonstrates, in this thesis, its superiority to the conventional eyes-based alignment. In a second approach, we use a geometric model through which the facial primitives are represented by Gabor filtering. Motivated by the fact that facial expressions are not only ambiguous and inconsistent across human but also dependent on the behavioral context; in this approach, we present a personalized facial expression recognition system whose overall performance is directly related to the localization performance of a set of facial fiducial points. These points are tracked through a sequence of video frames by a modification of a fast Gabor phase-based disparity estimation technique. In this thesis, we revisit the confidence measure, and introduce an iterative conditional procedure for displacement estimation that improves the robustness of the original methods.
Livres sur le sujet "Emotional filtering"
Pessoa, Luiz. Attention, Motivation, and Emotion. Sous la direction de Anna C. (Kia) Nobre et Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.001.
Texte intégralCox, Fiona. Mary Zimmerman. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779889.003.0006.
Texte intégralChapitres de livres sur le sujet "Emotional filtering"
Kwon, Hyeong-Joon, Hyeong-Oh Kwon et Kwang-Seok Hong. « Personalized Emotional Prediction Method for Real-Life Objects Based on Collaborative Filtering ». Dans Engineering Psychology and Cognitive Ergonomics, 45–52. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21741-8_6.
Texte intégralSalmeron-Majadas, Sergio, Miguel Arevalillo-Herráez, Olga C. Santos, Mar Saneiro, Raúl Cabestrero, Pilar Quirós, David Arnau et Jesus G. Boticario. « Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts ». Dans Lecture Notes in Computer Science, 429–38. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19773-9_43.
Texte intégralMatsumoto, Kazuyuki, Fuji Ren, Minoru Yoshida et Kenji Kita. « Refinement by Filtering Translation Candidates and Similarity Based Approach to Expand Emotion Tagged Corpus ». Dans Communications in Computer and Information Science, 260–80. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-52758-1_15.
Texte intégralNosshi, Anthony, Aziza Saad Asem et Mohammed Badr Senousy. « Hybrid Recommender System Using Emotional Fingerprints Model ». Dans Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, 1076–100. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch056.
Texte intégralMavelli, Luca. « The emotional value of refugees ». Dans Neoliberal Citizenship, 82–112. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857583.003.0004.
Texte intégralBisio, Igor, Alessandro Delfino, Fabio Lavagetto et Mario Marchese. « Opportunistic Detection Methods for Emotion-Aware Smartphone Applications ». Dans Creating Personal, Social, and Urban Awareness through Pervasive Computing, 53–85. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4695-7.ch003.
Texte intégralPetridis, Sergios, Theodoros Giannakopoulos et Constantine D. Spyropoulos. « A Low Cost Pupillometry Approach ». Dans Virtual and Mobile Healthcare, 765–77. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9863-3.ch037.
Texte intégralAkkarapatty, Neethu, Anjaly Muralidharan, Nisha S. Raj et Vinod P. « Dimensionality Reduction Techniques for Text Mining ». Dans Collaborative Filtering Using Data Mining and Analysis, 49–72. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0489-4.ch003.
Texte intégralAnitha R, Surya Koti Kiran A, Anurag K et Nikhil Y. « An Efficient Algorithm for Movie Recommendation System ». Dans Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210124.
Texte intégralActes de conférences sur le sujet "Emotional filtering"
Golbeck, Jennifer. « Improving Emotional Well-Being on Social Media with Collaborative Filtering ». Dans WebSci '20 : 12th ACM Conference on Web Science. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3394332.3402833.
Texte intégralJomaa, Inès, Emilie Poirson, Catherine Da Cunha et Jean-François Petiot. « Design of a Recommender System Based on Customer Preferences : A Comparison Between Two Approaches ». Dans ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82771.
Texte intégralAloufi, Ranya, Hamed Haddadi et David Boyle. « Emotion Filtering at the Edge ». Dans the 1st Workshop. New York, New York, USA : ACM Press, 2019. http://dx.doi.org/10.1145/3362743.3362960.
Texte intégralPathak, Bageshree Sathe, Manali Sayankar et Ashish Panat. « Emotion transformation from neutral to 3 emotions of speech signal using DWT and adaptive filtering techniques ». Dans 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030389.
Texte intégralKim, Tae-Yeun, et Sung-Hwan Kim. « Emotion and Collaborative Filtering-Based Recommendation System ». Dans SMA 2020 : The 9th International Conference on Smart Media and Applications. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3426020.3426119.
Texte intégralHuang, Zhaocheng, et Julien Epps. « An Investigation of Emotion Dynamics and Kalman Filtering for Speech-Based Emotion Prediction ». Dans Interspeech 2017. ISCA : ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1707.
Texte intégralHuang, Dong, Haihong Zhang, Kaikeng Ang, Cuntai Guan, Yaozhang Pan, Chuanchu Wang et Juanhong Yu. « Fast emotion detection from EEG using asymmetric spatial filtering ». Dans ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6287952.
Texte intégralAloufi, Ranya, Hamed Haddadi et David Boyle. « Privacy preserving speech analysis using emotion filtering at the edge ». Dans SenSys '19 : The 17th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA : ACM, 2019. http://dx.doi.org/10.1145/3356250.3361947.
Texte intégralPetrantonakis, Panagiotis C., et Leontios J. Hadjileontiadis. « EEG-based emotion recognition using hybrid filtering and higher order crossings ». Dans 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009). IEEE, 2009. http://dx.doi.org/10.1109/acii.2009.5349513.
Texte intégralShiqing Zhang et Zhijin Zhao. « Feature selection filtering methods for emotion recognition in Chinese speech signal ». Dans 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697464.
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