Relevant bibliographies by topics / Emotional filtering

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Emotional filtering'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Emotional filtering"

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Ismatun Nisak, Isnatul Mukarromah, Luthfiyah Mesi Aditama, and Muhammad Nofan Zulfahmi. "Pentingnya Filterisasi Konten Dewasa pada Perkembangan Sosial Emosional Anak Sekolah Dasar." Jurnal Bintang Pendidikan Indonesia 3, no. 1 (2024): 199–209. https://doi.org/10.55606/jubpi.v3i1.3580.

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This study aims to provide information and insight into the importance of filtering adult content in the social-emotional development of elementary school children. In today's digital era, technological advances make it easy to access information through various devices such as gadgets. This shows both positive and negative impacts on education, especially if there is no filtering, so children are free to search for information such as adult content that can affect their social emotional development. This study uses a qualitative approach to explain the importance of adult content filtering in
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RULE, R. R., A. P. SHIMAMURA, and R. T. KNIGHT. "Orbitofrontal cortex and dynamic filtering of emotional stimuli." Cognitive, Affective, & Behavioral Neuroscience 2, no. 3 (2002): 264–70. http://dx.doi.org/10.3758/cabn.2.3.264.

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Prete, Giulia, Bruno Laeng, and Luca Tommasi. "Modulating adaptation to emotional faces by spatial frequency filtering." Psychological Research 82, no. 2 (2016): 310–23. http://dx.doi.org/10.1007/s00426-016-0830-x.

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Jenkins, Jeffrey. "Detecting emotional ambiguity in text." MOJ Applied Bionics and Biomechanics 4, no. 3 (2020): 55–57. http://dx.doi.org/10.15406/mojabb.2020.04.00134.

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An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal
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Kadiri, 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.

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Denis, Patrice, Vincent Courboulay, Arnaud Revel, Syntyche Gbèhounou, François Lecellier, and Christine Fernandez-Maloigne. "Improvement of natural image search engines results by emotional filtering." EAI Endorsed Transactions on Creative Technologies 3, no. 6 (2016): 151164. http://dx.doi.org/10.4108/eai.25-4-2016.151164.

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Sarwath Unnisa and Akshaya N S. "Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering." International Journal of Information Technology, Research and Applications 3, no. 3 (2024): 15–22. http://dx.doi.org/10.59461/ijitra.v3i3.100.

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This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and
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Shinde, Vishakha Rajan, and Dr Arati Deshpande. "A Brief Review on Audiobook Recommendation System Based on Contextual and Emotional Cues." International Journal of Research and Innovation in Applied Science X, no. VI (2025): 1341–45. https://doi.org/10.51584/ijrias.2025.100600102.

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The exponential growth of audio book platforms has resulted in an overwhelming volume of content, necessitating intelligent recommendation systems that go beyond traditional filtering approaches. Conventional collaborative and content-based filtering methods often overlook critical aspects such as user emotions and real-time contextual factors, leading to suboptimal personalization. This survey presents a comprehensive review of recent developments in audio book recommendation systems that incorporate hybrid deep learning models, emotion recognition, and context- awareness. It evaluates variou
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Xu, Qinfu, Shaozu Yuan, Yiwei Wei, Jie Wu, Leiquan Wang, and Chunlei Wu. "Multiple Feature Refining Network for Visual Emotion Distribution Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 8924–32. https://doi.org/10.1609/aaai.v39i9.32965.

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The significance of visual emotion distribution learning (VEDL) has surged, particularly with the growing inclination to convey emotions through images. The key of VEDL lies in capturing both low- and high-level features within the same visual content, thus promoting the model for salient and subtle emotion awareness. To learn the distribution of emotions involved in images, most previous works learn coarse semantic knowledge with unbiased filtering. Consequently, they focus on the entire scene and suffer from the redundancy of semantic-irrelevant information, which diminishes the affective co
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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 (2021): 1997. http://dx.doi.org/10.3390/s21061997.

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Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised,
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Dissertations / Theses on the topic "Emotional filtering"

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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.

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<p>This 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.</p><p>The fir
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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.

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Detection of a human emotion from human speech is always a challenging task. Factors like intonation, pitch, and loudness of signal vary from different human voice. So, it's important to know the exact pitch, intonation and loudness of a speech for making it a challenging task for detection. Some voices exhibit high background noise which will affect the amplitude or pitch of the signal. So, knowing the detailed properties of a speech to detect emotion is mandatory. Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been app
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Houdek, 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.

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This master thesis concerns with emotional states and gender recognition on the basis of speech signal analysis. We used various prosodic and cepstral features for the description of the speech signal. In the text we describe non-invasive methods for glottal pulses estimation. The described features of speech were implemented in MATLAB. For their classification we used the GMM classifier, which uses the Gaussian probability distribution for modeling a feature space. Furthermore, we constructed a system for recognition of emotional states of the speaker and a system for gender recognition from
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Patacca, Alessia. "The impact of emotional stressors on distractor filtering." Doctoral thesis, 2019. http://hdl.handle.net/11562/995343.

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Human beings constantly deal with an enormous amount of information that cannot be processed at once. Given the limited cognitive resources available for the processing of incoming information, visual selective attention has the role to differentiate between competing stimuli in order to facilitate the processing of stimuli that are relevant for adaptive behaviours. From an evolutionary perspective, stimuli with emotional content, in particular those signalling danger or threat, are very powerful in attracting and holding attention even if they are task-irrelevant. Moreover, emotional stimuli
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Dahmane, Mohamed. "Analyse de mouvements faciaux à partir d'images vidéo." Thèse, 2011. http://hdl.handle.net/1866/7120.

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Lors d'une intervention conversationnelle, le langage est supporté par une communication non-verbale qui joue un rôle central dans le comportement social humain en permettant de la rétroaction et en gérant la synchronisation, appuyant ainsi le contenu et la signification du discours. En effet, 55% du message est véhiculé par les expressions faciales, alors que seulement 7% est dû au message linguistique et 38% au paralangage. L'information concernant l'état émotionnel d'une personne est généralement inférée par les attributs faciaux. Cependant, on ne dispose pas vraiment d'instruments de mesur
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Books on the topic "Emotional filtering"

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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.

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The first part of the chapter describes effects of motivation on attention at the behavioural and physiological levels. For example, reward increases detection sensitivity (dprime) in both endogenous attention and exogenous attention tasks, enhances stimulus coding, and influences the filtering of task-irrelevant stimuli. These recent findings are surprising insofar as traditional psychological models have described motivation as a fairly unspecific ‘force’. The results reviewed are far from global. Instead they reflect specific mechanisms that are manifested selectively both at behavioural an
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Cox, Fiona. Mary Zimmerman. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779889.003.0006.

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Mary Zimmerman’s dramatic adaptation of the Metamorphoses (1998) follows the staging of the Odyssey and represents a further instance of her ongoing engagement with the classical world. By filtering the Ovidian myths through a network of allusions (to Rilke, Jung, and Freud, among others) she reminds her audiences of the consolidation of classical myth within the Western tradition. At the same time she uses the play to meditate upon issues such as unbridled greed and capitalism, anorexia, and emotionally damaged ‘rich kids’. Her play is also underpinned by a profound sense of loss and grief. T
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Book chapters on the topic "Emotional filtering"

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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. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21741-8_6.

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Benrouba, Ferdaous, and Rachid Boudour. "Mitigating Emotional Harm on Social Media: A Filtering Approach Using Synesketch and Euclidean Distance." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4484-2_20.

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Wang, Zhihong. "Emotional Meaning in the Art Design of Architectural Environment Based on Distributed Collaborative Filtering Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1428-9_238.

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Salmeron-Majadas, Sergio, Miguel Arevalillo-Herráez, Olga C. Santos, et al. "Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19773-9_43.

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Matsumoto, 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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-52758-1_15.

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Chen, Biyun, Lin Jiang, Xin Pan, Guoquan Zhou, Aihua Sun, and Dafang Li. "Exploring Emotion Trends in Product Reviews: A Multi-modal Analysis with Malicious Comment Filtering and User Privacy Protection." In Information Security and Cryptology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0942-7_19.

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Nosshi, 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. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch056.

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With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of th
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Mavelli, Luca. "The emotional value of refugees." In Neoliberal Citizenship. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857583.003.0004.

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This chapter further explores the notion of ‘emotional value’ introduced in the previous chapter. To this end, it builds on the existing literature on ‘humanitarian government’ by showing how the growing intermingling of humanitarianism and security is part of a process of filtering aimed at granting protection only to refugees regarded as valuable. The chapter elaborates on how the notion of ‘ideal refugee’ signals an ultimate commodification of solidarity which subordinates the demand of refugees to their perceived market value. It shows how these neoliberal rationalities of value are mobili
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Bisio, 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. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4695-7.ch003.

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Human-machine interaction is performed by devices such as the keyboard, the touch-screen, or speech-to-text applications. For example, a speech-to-text application is software that allows the device to translate the spoken words into text. These tools translate explicit messages but ignore implicit messages, such as the emotional status of the speaker, filtering out a portion of information available in the interaction process. This chapter focuses on emotion detection. An emotion-aware device can also interact more personally with its owner and react appropriately according to the user’s mood
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Chun, Marvin M., and Steven B. Most. "External Attention." In Cognition. Oxford University Press, 2021. https://doi.org/10.1093/hesc/9780199950638.003.0004.

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This chapter delves into the complex nature of attention, explaining it as a collection of cognitive mechanisms that enable individuals to focus on relevant stimuli while filtering out the irrelevant. A core function, selection allows the prioritisation of certain information, influenced by factors such as spatial attention, temporal attention, or feature-based attention. Debates on whether attention operates early (filtering by physical traits) or late (post-meaning processing) highlight the flexibility of selection depending on cognitive demands. Attention also modulates perception, enhancin
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Conference papers on the topic "Emotional filtering"

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Peñaredondo, Kiann, Jaspher Camu, Criselle Centeno, Mark Anthony Mercado, Vivien Agustin, and Mary Grace Gonzales. "EmoShown: AI-Powered Emotional Wellness Hub with Sentiment Analysis, Anomaly Detection, and Collaborative Filtering." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10912919.

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L. U.S. L, Perera U., H. M. Samadhi Chathuranga, and Thusithanjana Thilakarathna. "Personalized Book Recommendation Engine with Emotional Understanding and Contextual Relevance Based on Hybrid Filtering - A Novel Method Providing Recommendation Explanation." In 2025 7th International Conference on Software Engineering and Computer Science (CSECS). IEEE, 2025. https://doi.org/10.1109/csecs64665.2025.11009793.

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Sachan, Anjali, Deependra Pandey, and Neha Mittal. "Facial Emotion Recognition using Image Filtering Techniques." In 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC). IEEE, 2024. https://doi.org/10.1109/sparc61891.2024.10828651.

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Zhao, Huan, Yingxue Gao, Haijiao Chen, Bo Li, Guanghui Ye, and Zixing Zhang. "Enhanced Multimodal Emotion Recognition in Conversations via Contextual Filtering and Multi-Frequency Graph Propagation." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888592.

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Golbeck, Jennifer. "Improving Emotional Well-Being on Social Media with Collaborative Filtering." In WebSci '20: 12th ACM Conference on Web Science. ACM, 2020. http://dx.doi.org/10.1145/3394332.3402833.

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Jomaa, 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.

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This work addresses the design of a preference based system that suggests relevant products to customers. It aims at helping them with their purchase decision (on electronic commerce websites). A use case that consists in making spontaneous recommendations to the customers, on the basis of their previous ratings is described. The product considered to illustrate the approach is a comic. This paper is focused on two recommender approaches. The first approach, “the traditional” approach, is based on the collaborative filtering while the second approach, is based on a new proposed algorithm. Coll
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Okazaki, Kanji, Keiichi Watanuki, and Yusuke Osawa. "Development of a Fast and High-Precision Audio Noise Reduction System to Enhance the Accuracy of Emotion Estimation in Practical Applications." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006058.

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Speech-based emotion estimation has diverse applications, including mental health monitoring, human–computer interaction, and communication enhancement. The accurate estimation of emotions from speech is crucial in the detection of psychological stress, which is a growing concern in today’s high-stress societies. However, environmental noise significantly degrades estimation accuracy, and studies focusing on noise reduction specifically optimized for emotion estimation remain scarce. This study evaluated the impact of noise reduction on emotion estimation by comparing traditional signal proces
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Aloufi, Ranya, Hamed Haddadi, and David Boyle. "Emotion Filtering at the Edge." In the 1st Workshop. ACM Press, 2019. http://dx.doi.org/10.1145/3362743.3362960.

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Pathak, 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.

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Kim, 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. ACM, 2020. http://dx.doi.org/10.1145/3426020.3426119.

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