Academic literature on the topic 'Emotional filtering'
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Journal articles on the topic "Emotional filtering"
Kim, Tae-Yeun, Hoon Ko, Sung-Hwan Kim, and Ho-Da Kim. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6 (March 12, 2021): 1997. http://dx.doi.org/10.3390/s21061997.
Full textJenkins, Jeffrey. "Detecting emotional ambiguity in text." MOJ Applied Bionics and Biomechanics 4, no. 3 (May 25, 2020): 55–57. http://dx.doi.org/10.15406/mojabb.2020.04.00134.
Full textRULE, R. R., A. P. SHIMAMURA, and R. T. KNIGHT. "Orbitofrontal cortex and dynamic filtering of emotional stimuli." Cognitive, Affective, & Behavioral Neuroscience 2, no. 3 (September 1, 2002): 264–70. http://dx.doi.org/10.3758/cabn.2.3.264.
Full textGuerzoni, Michael A. "Vicarious trauma and emotional labour in researching child sexual abuse and child protection: A postdoctoral reflection." Methodological Innovations 13, no. 2 (May 2020): 205979912092634. http://dx.doi.org/10.1177/2059799120926342.
Full textNosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." International Journal of Information Retrieval Research 9, no. 3 (July 2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.
Full textSantamaria-Granados, Luz, Juan Francisco Mendoza-Moreno, Angela Chantre-Astaiza, Mario Munoz-Organero, and Gustavo Ramirez-Gonzalez. "Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data." Sensors 21, no. 23 (November 25, 2021): 7854. http://dx.doi.org/10.3390/s21237854.
Full textPrete, Giulia, Bruno Laeng, and Luca Tommasi. "Modulating adaptation to emotional faces by spatial frequency filtering." Psychological Research 82, no. 2 (November 26, 2016): 310–23. http://dx.doi.org/10.1007/s00426-016-0830-x.
Full textWang, Shu, Chonghuan Xu, Austin Shijun Ding, and Zhongyun Tang. "A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network." Electronics 10, no. 15 (July 24, 2021): 1769. http://dx.doi.org/10.3390/electronics10151769.
Full textMičieta, Branislav, Vladimíra Biňasová, Beáta Furmannová, Gabriela Gabajová, and 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.
Full textKadiri, Sudarsana Reddy, and B. Yegnanarayana. "Epoch extraction from emotional speech using single frequency filtering approach." Speech Communication 86 (February 2017): 52–63. http://dx.doi.org/10.1016/j.specom.2016.11.005.
Full textDissertations / Theses on the topic "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.
Full textThis 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, and 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.
Full textHoudek, 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.
Full textPatacca, Alessia. "The impact of emotional stressors on distractor filtering." Doctoral thesis, 2019. http://hdl.handle.net/11562/995343.
Full textDahmane, Mohamed. "Analyse de mouvements faciaux à partir d'images vidéo." Thèse, 2011. http://hdl.handle.net/1866/7120.
Full textIn 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.
Books on the topic "Emotional filtering"
Pessoa, Luiz. Attention, Motivation, and Emotion. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.001.
Full textCox, Fiona. Mary Zimmerman. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779889.003.0006.
Full textBook chapters on the topic "Emotional filtering"
Kwon, Hyeong-Joon, Hyeong-Oh Kwon, and Kwang-Seok Hong. "Personalized Emotional Prediction Method for Real-Life Objects Based on Collaborative Filtering." In 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.
Full textSalmeron-Majadas, Sergio, Miguel Arevalillo-Herráez, Olga C. Santos, Mar Saneiro, Raúl Cabestrero, Pilar Quirós, David Arnau, and Jesus G. Boticario. "Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts." In Lecture Notes in Computer Science, 429–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19773-9_43.
Full textMatsumoto, Kazuyuki, Fuji Ren, Minoru Yoshida, and Kenji Kita. "Refinement by Filtering Translation Candidates and Similarity Based Approach to Expand Emotion Tagged Corpus." In 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.
Full textNosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." In 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.
Full textMavelli, Luca. "The emotional value of refugees." In Neoliberal Citizenship, 82–112. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857583.003.0004.
Full textBisio, Igor, Alessandro Delfino, Fabio Lavagetto, and Mario Marchese. "Opportunistic Detection Methods for Emotion-Aware Smartphone Applications." In 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.
Full textPetridis, Sergios, Theodoros Giannakopoulos, and Constantine D. Spyropoulos. "A Low Cost Pupillometry Approach." In Virtual and Mobile Healthcare, 765–77. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9863-3.ch037.
Full textAkkarapatty, Neethu, Anjaly Muralidharan, Nisha S. Raj, and Vinod P. "Dimensionality Reduction Techniques for Text Mining." In Collaborative Filtering Using Data Mining and Analysis, 49–72. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0489-4.ch003.
Full textAnitha R, Surya Koti Kiran A, Anurag K, and Nikhil Y. "An Efficient Algorithm for Movie Recommendation System." In Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210124.
Full textConference papers on the topic "Emotional filtering"
Golbeck, Jennifer. "Improving Emotional Well-Being on Social Media with Collaborative Filtering." In WebSci '20: 12th ACM Conference on Web Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394332.3402833.
Full textJomaa, Inès, Emilie Poirson, Catherine Da Cunha, and Jean-François Petiot. "Design of a Recommender System Based on Customer Preferences: A Comparison Between Two Approaches." In 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.
Full textAloufi, Ranya, Hamed Haddadi, and David Boyle. "Emotion Filtering at the Edge." In the 1st Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3362743.3362960.
Full textPathak, Bageshree Sathe, Manali Sayankar, and Ashish Panat. "Emotion transformation from neutral to 3 emotions of speech signal using DWT and adaptive filtering techniques." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030389.
Full textKim, Tae-Yeun, and Sung-Hwan Kim. "Emotion and Collaborative Filtering-Based Recommendation System." In 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.
Full textHuang, Zhaocheng, and Julien Epps. "An Investigation of Emotion Dynamics and Kalman Filtering for Speech-Based Emotion Prediction." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1707.
Full textHuang, Dong, Haihong Zhang, Kaikeng Ang, Cuntai Guan, Yaozhang Pan, Chuanchu Wang, and Juanhong Yu. "Fast emotion detection from EEG using asymmetric spatial filtering." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6287952.
Full textAloufi, Ranya, Hamed Haddadi, and David Boyle. "Privacy preserving speech analysis using emotion filtering at the edge." In 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.
Full textPetrantonakis, Panagiotis C., and Leontios J. Hadjileontiadis. "EEG-based emotion recognition using hybrid filtering and higher order crossings." In 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.
Full textShiqing Zhang and Zhijin Zhao. "Feature selection filtering methods for emotion recognition in Chinese speech signal." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697464.
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