Relevant bibliographies by topics / Music and mood

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

Academic literature on the topic 'Music and mood'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Music and mood"

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Dolan, J. D. "Mood Music." Antioch Review 53, no. 1 (1995): 78. http://dx.doi.org/10.2307/4613085.

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Moon, Chang Bae, HyunSoo Kim, Dong Won Lee, and Byeong Man Kim. "Mood lighting system reflecting music mood." Color Research & Application 40, no. 2 (December 11, 2013): 201–12. http://dx.doi.org/10.1002/col.21864.

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Garvey, Debbie. "Making mood music." 5 to 7 Educator 2009, no. 57 (September 2009): xviii—xix. http://dx.doi.org/10.12968/ftse.2009.8.9.44061.

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Mali, A. S., A. A. Kenjale, P. M. Ghatage, and A. G. Deshpande. "Mood based Music System." International Journal of Scientific Research in Computer Science and Engineering 6, no. 3 (June 30, 2018): 27–30. http://dx.doi.org/10.26438/ijsrcse/v6i3.2730.

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II, Gordon C. Bruner. "Music, Mood, and Marketing." Journal of Marketing 54, no. 4 (October 1990): 94. http://dx.doi.org/10.2307/1251762.

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Yadav, Satyapal, and Akash Saxena. "Music Based Mood Classification." International Journal of Computer Trends and Technology 48, no. 3 (June 25, 2017): 139–47. http://dx.doi.org/10.14445/22312803/ijctt-v48p127.

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Bruner, Gordon C. "Music, Mood, and Marketing." Journal of Marketing 54, no. 4 (October 1990): 94–104. http://dx.doi.org/10.1177/002224299005400408.

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Jun, Sanghoon, Seungmin Rho, and Eenjun Hwang. "Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences." International Journal on Semantic Web and Information Systems 6, no. 2 (April 2010): 1–16. http://dx.doi.org/10.4018/jswis.2010040101.

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A typical music clip consists of one or more segments with different moods and such mood information could be a crucial clue for determining the similarity between music clips. One representative mood has been selected for music clip for retrieval, recommendation or classification purposes, which often gives unsatisfactory result. In this paper, the authors propose a new music retrieval and recommendation scheme based on the mood sequence of music clips. The authors first divide each music clip into segments through beat structure analysis, then, apply the k-medoids clustering algorithm for grouping all the segments into clusters with similar features. By assigning a unique mood symbol for each cluster, one can transform each music clip into a musical mood sequence. For music retrieval, the authors use the Smith-Waterman (SW) algorithm to measure the similarity between mood sequences. However, for music recommendation, user preferences are retrieved from a recent music playlist or user interaction through the interface, which generates a music recommendation list based on the mood sequence similarity. The authors demonstrate that the proposed scheme achieves excellent performance in terms of retrieval accuracy and user satisfaction in music recommendation.
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Yang, Zhong Liang, Yu Miao Chen, and Rui Min Lyu. "A Comparative Study on Driving Performance Induced by Music Mood for Development of In-Vehicle Media Players." Applied Mechanics and Materials 494-495 (February 2014): 108–11. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.108.

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Though music listening while driving is a popular activity, it is not clear the effects of music mood induction on driving performance in different road conditions. In this study, four music samples with strongest mood features were selected by simplified Delphi method. Then driving performance in two road conditions induced by five music moods were investigated by comparative experiments. Results of ANOVA preliminarily demonstrated the significant differences between driving performance induced by various music mood in different road conditions. Furthermore, a mobile music player was developed and installed in two intelligence assist vehicles to validate its usability in the practice.
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Xu, Xin, Hui Guan, Zhen Liu, and Bo Jun Wang. "EEG-Based Music Mood Analysis and Applications." Advanced Materials Research 712-715 (June 2013): 2726–30. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2726.

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Music is known to be a powerful elicitor of emotions. Music with different moods induces various emotions, each of which corresponding to certain pattern of EEG signals. In this paper, based on current music mood categories, we discuss how the music belonging to different mood types affect the pattern EEG activity. We review several literatures verifying that certain characteristics of EEG differ from each other induced by different types of music. Such differences make it possible for emotion recognition through EEG signals. We also introduce some applications of emotional music such as improvement of human emotions and adjuvant treatment of diseases.
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Dissertations / Theses on the topic "Music and mood"

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Hewston, Ruth. "Using music to regulate pre-performance mood." Thesis, University of Wolverhampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413455.

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Economides, Alexander. "How Music Makes Us Feel." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/philosophy_theses/106.

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According to folk psychology, instrumental music regularly elicits emotions in listeners. Philosophers and psychologists such as Kivy, Konecni and Zangwill have questioned the existence of these musically elicited emotions, arguing that instrumental music elicits moods or aesthetic judgments rather than emotions. I defend the folk psychological position against these skeptics. The first chapter sets up the debate surrounding musically elicited emotions, while chapters two and three defend the thesis that instrumental music elicits emotions against the critics’ arguments. Chapter four outlines the implications of this defense for a variety of fields.
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Meyers, Owen Craigie. "A mood-based music classification and exploration system." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39337.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.
Includes bibliographical references (p. 89-93).
Mood classification of music is an emerging domain of music information retrieval. In the approach presented here features extracted from an audio file are used in combination with the affective value of song lyrics to map a song onto a psychologically based emotion space. The motivation behind this system is the lack of intuitive and contextually aware playlist generation tools available to music listeners. The need for such tools is made obvious by the fact that digital music libraries are constantly expanding, thus making it increasingly difficult to recall a particular song in the library or to create a playlist for a specific event. By combining audio content information with context-aware data, such as song lyrics, this system allows the listener to automatically generate a playlist to suit their current activity or mood.
by Owen Craigie Meyers.
S.M.
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Mazzoni, Antonella. "Mood Glove : enhancing mood in film music through haptic sensations for an enriched film experience." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/39757.

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This research explores a new way of enhancing audience experience in film entertainment, presenting the design and implementation of a wearable prototype system that uses haptic sensations to intensify moods in lm music. The aim of this work is to enrich the musical experience of film audiences and might also have implications on the hearing-impaired, providing them with a new enhanced emotional experience while watching a movie. Although there has been previous work into music displays of a visual and haptic nature, and on the importance of music in film, there is no documented research on musical enhancement experience in film entertainment. This work focuses on the mood conveyed by film music in order to understand what role it plays in creating the film experience, and also explores the possibility of enhancing those feelings through haptic sensations. Drawing on HCI and interaction design principles, the design of a piece of haptic wearable technology is proposed and used as the tool for user studies. This research contributes to the fields of: HCI, interaction design, user experience design, multimodal interaction, creative technology, wearable technology, haptics, entertainment technology and film music. This work also provides a set of design suggestions to aid future research and designers of haptic sensations for media enhancement. Proposed guidelines are based on a number of empirical findings that describe and explain aspects of audience emotional response to haptics, providing some first evidence that there is a correlation between vibrotactile stimuli (such as frequency and intensity) and perceived feelings.
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Irobunda, Cynthia. "The Effect of Mood on Persuasion: The Role of Music and Dance in Mood Induction." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/scripps_theses/1090.

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Previous literature has shown how mood influences persuasion. The aim of this study is to investigate how the persuasion and elaboration of strong and weak arguments are increased or decreased by positive and negative moods, that will be induced through distinct video selections. Participants will be asked to watch a video that incorporates specific music and dancing to induce a positive or negative mood. This study is a 2 mood induction (positive vs. negative) X 2 argument strength (strong vs. weak) design. The results of the two-way ANOVA will show that elaboration is suspected to decrease when one is in a positive mood, but increase in a negative mood. Strong arguments are expected to be more persuasive than weak arguments. Mood and dance will successfully induce positive and negative mood. Mood and argument strength will influence persuasion and elaboration. Implications for this study are expected to further research on how to use music and dance in persuasive appeals, and how mood plays a role in emotional decision-making, especially within advertisements.
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Cole, Wendy Robin. "Effects of verbal suggestion and music on mood : [a thesis]." Scholarly Commons, 1988. https://scholarlycommons.pacific.edu/uop_etds/2166.

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This study examined the effects of verbal suggestion and music listening on mood changes of college students. The 135 volunteers were randomly assigned to one of three conditions (Condition 1/somber depiction with music, Condition 2/pleasant depiction with music, or Condition 3/music only). The Profile of Mood States was administered before and after each condition. Significant differences (p<.05) were found between pre and posttest scores within the groups; however, no significant differences were found among the groups for these same measures. A music questionnaire was administered following the posttest which examined the subjects' preference for the music played, the frequency of experiencing images while listening to music, and their opinion concerning whether the depiction complemented the music. Implications of the results are discussed, and suggestions for future research are given.
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Romiti, Jonathan Scott. "Beyond Mood Congruence: Effects of Music on Memory in Film." Thesis, Boston College, 2008. http://hdl.handle.net/2345/553.

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Thesis advisor: Elizabeth A. Kensinger
Thesis advisor: Ellen Winner
Like it or not, music is everywhere. Our lives are accompanied by an omnipresent personal soundtrack—whether we are on our way to work, at the grocery store, at a movie, watching TV, or simply relaxing at home. It seems that the only way human beings have been able to tolerate this extraneous stimulus is, simply, by not consciously attending to it. Otherwise, we would most likely crash our cars, purchase the wrong items at the store, and never quite understand what happened in that movie we had just seen. Despite the technological advances in music recording and production (and, in turn, availability), very little psychological research has focused on the effects of music processing (especially at the unconscious level) on memory consolidation and storage. What previous memory research has shown is that human beings tend to exhibit an attentional enhancement for emotional stimuli when presented alongside non-emotional stimuli (Reisberg & Heuer 2004). Specifically, this finding has demonstrated that emotional events promote memory for "central" components of an event, while having a reverse effect for an event's "periphery." In the current study, I employed the medium of film in order to apply this hypothesis to our musical world. Participants were randomly assigned to one of three groups: 1) a "no music" group, in which participants viewed a film clip in silence; 2) a "with music" group, in which participants viewed the same clip with the film's original, low-quality (and low arousal) recorded soundtrack; and 3) a "re-mastered music" group, in which participants viewed the film clip with a higher quality (and higher arousal) soundtrack. Three main results were found, all of which either aligned with or extended the findings of Reisberg & Heuer to include the domain of music as a modulatory force in the formation of emotional memories
Thesis (BA) — Boston College, 2008
Submitted to: Boston College. College of Arts and Sciences
Discipline: Psychology
Discipline: College Honors Program
Discipline: Psychology Honors Program
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Johansson, Pontus. "Does quartal harmony affect the mood in computer games?" Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16096.

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The background explains musical concepts of consonance, dissonance and quartal harmony which are the central parts of this work. Emotion in- and application of music is explained as well, to give an understanding to how the music is being used. The problem is what was studied, and the method shows how it was to be studied. The goal of this work was to see what kind of harmony is better at inducing stress in a player playing a game, quartal or triad harmony. Two different harmonizations of the same songs were composed and implemented into a game sequence. Semi-structured qualitative interviews were held to collect data. The result of this study showed that both harmonizations induced the same amount of stress, not leading to any concrete answer.
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Priest, David-Lee. "Characteristics and effects of motivational music in exercise." Thesis, Brunel University, 2004. http://bura.brunel.ac.uk/handle/2438/2914.

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The research programme had three principal objectives. First, the evaluation and extension of the extant conceptual framework pertaining to motivational music in exercise settings. Second, the development of a valid instrument for assessing the motivational qualities of music: The Brunel Music Rating Inventory-2 (BMRI-2). Third, to test the effects of motivational and oudeterous (lacking in both motivational and de-motivational qualities) music in an externally-valid setting. These objectives were addressed through 4 studies. First, a series of open-ended interviews were conducted with exercise leaders and participants (N = 13), in order to investigate the characteristics and effects of motivational music in the exercise setting. The data were content analysed to abstract thematic categories of response. These categories were subsequently evaluated in the context of relevant conceptual frameworks. Subsequently, a sample of 532 health-club members responded to a questionnaire that was designed to assess the perceived characteristics of motivational music. The responses were analysed across age groups, gender, frequency of attendance (low, medium, high), and time of attendance (morning, afternoon, evening). The BMRI-2 was developed in order to address psychometric weaknesses that were associated with its forbear, the BMRI. A refined item pool was created which yielded an 8-item instrument that was subjected to confirmatory factor analysis. A single-factor model demonstrated acceptable fit indices across three different pieces of music, two samples of exercise participants, and both sexes. The BMRI-2 was used to select 20 pieces of motivational music, which were delivered in a health club gymnasium. It was found that health club members (N = 112) exercised for longer under the condition of motivational music as opposed to oudeterous music (the club’s typical output); however, no differences were noted in terms of affective response. (Jun 2004)
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Sanchez, Robert-Christian. "The effect of music on physiological responses and self-perceived mood." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/612.

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Music is often studied in terms of its artistic value and expressiveness. While these are important characteristics, there are other observations we can make of scientific value, such as the effects of music on the human anatomy. At present, however, there is a general lack of scientific studies focusing on the effect music makes on specific physiological responses in the body. A limited range of these studies has included examinations of music preferences and correlating personality characteristics of participants, while some others have investigated the effects of music lessons on intelligence. While the previously mentioned research has contributed to some scientific understanding with regard to human physiology, it has not addressed how specific physiological processes of the human body responds to music. Through my own research, I hope to add to the body of musical research and health sciences, and help to close the gap between these two fields. In this study, one selection from a pool of five audio examples of different musical genres will be played to participants while their heart rate is monitored with a wrist-worn device in order to gauge possible differences in heart rate that might occur as they experience music. This assessment will also be paired with a self-perceived mood questionnaire by my participants in order to identify any correlations between the two. After my data collection is complete, I will statistically analyze the information and examine any parallels between the musical selections, genre, heart rate, and self-perceived mood. By analyzing this data, I hope to gain insight into possible human physiological responses as the subjects are exposed to different musical examples from various genres. I also hope to investigate the psychological realm of music, and determine its practical use in the medical field with regard to music therapy, which can lead to various treatments in post-traumatic stress disorder, and communicative diseases.
B.M.
Bachelors
Arts and Humanities
Music Performance
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Books on the topic "Music and mood"

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Harcourt, Nic. Music lust: Recommended listening for every mood, moment, and reason. Seattle: Sasquatch Books, 2005.

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Wood, W. W. (Willy Wray), 1957-, ed. The rock 'n' roll classroom: Using music to manage mood, energy, and learning. Thousand Oaks, Calif: Corwin Press, 2013.

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Music in every room: Around the world in a bad mood. New York: Atlantic Monthly Press, 1988.

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Mood Management durch Musik: Die alltägliche Nutzung von Musik zur Regulierung von Stimmungen. Köln: H. von Halem, 2005.

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Lefebvre, Brandon F. A. The effect of familiarity, mood, and gender on memory in regards to music. Sudbury, Ont: Laurentian University, 2005.

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Wales, Douglas N. The effects of tempo and disposition in music on perceived exertion, brain waves and mood during aerobic exercise. Eugene: Microform Publications, College of Human Development and Performance, University of Oregon, 1987.

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Moor music. Bridgend, Wales: Seren, 2010.

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Faye, Kellerman. Moon music. London: Headline, 1998.

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Faye, Kellerman. Moon music. Rockland, MA: Wheeler Pub., 1998.

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Faye, Kellerman. Moon music. London: Headline Feature, 1998.

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Book chapters on the topic "Music and mood"

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Scirea, Marco. "Mood Dependent Music Generator." In Lecture Notes in Computer Science, 626–29. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03161-3_68.

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Vandana, Tula, Nara Kalyani, and K. Santhi Sree. "Music Mood Categorization: A Survey." In ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, 120–32. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8461-5_14.

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Ketovuori, Mikko, and Matt Lampert. "From ‘Hard Rock Hallelujah’ to ‘Ukonhauta’ in Nokialand—A Socionomic Perspective on the Mood Shift in Finland’s Popular Music from 2006 to 2009." In Mood, 109–26. New York : Taylor and Francis, 2019. |: Routledge, 2019. http://dx.doi.org/10.4324/9780429259432-7.

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Garrido, Sandra. "Mood Regulation Disorders: An Exception to Mood Management Theory?" In Why Are We Attracted to Sad Music?, 129–48. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39666-8_8.

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Das, Sanchali, Sambit Satpathy, and Swapan Debbarma. "Multimodal Music Mood Classification Framework for Kokborok Music." In Advances in Intelligent Systems and Computing, 163–75. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4299-6_14.

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Tong, Haoyue, Min Zhang, Pouneh Soleimaninejadian, Qianfan Zhang, Kailu Wu, Yiqun Liu, and Shaoping Ma. "Music Mood Classification Based on Lifelog." In Lecture Notes in Computer Science, 55–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01012-6_5.

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Çano, Erion, Riccardo Coppola, Eleonora Gargiulo, Marco Marengo, and Maurizio Morisio. "Mood-Based On-Car Music Recommendations." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 154–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52569-3_14.

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Urmi, Nazia Parvin, Nasir Uddin Ahmed, Md Habibur Rahman Sifat, Salekul Islam, and Abu Shafin Mohammad Mahdee Jameel. "BanglaMusicMooD: A Music Mood Classifier from Bangla Music Lyrics." In International Conference on Mobile Computing and Sustainable Informatics, 673–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49795-8_64.

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Patra, Braja Gopal, Dipankar Das, and Sivaji Bandyopadhyay. "Unsupervised Approach to Hindi Music Mood Classification." In Mining Intelligence and Knowledge Exploration, 62–69. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_7.

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Loviscach, Jörn, and David Oswald. "In the Mood: Tagging Music with Affects." In Affect and Emotion in Human-Computer Interaction, 220–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85099-1_19.

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Conference papers on the topic "Music and mood"

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Arora, Anuja, Aastha Kaul, and Vatsala Mittal. "Mood Based Music Player." In 2019 International Conference on Signal Processing and Communication (ICSC). IEEE, 2019. http://dx.doi.org/10.1109/icsc45622.2019.8938384.

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Ujlambkar, Aniruddha M., and Vahida Z. Attar. "Mood classification of Indian popular music." In the CUBE International Information Technology Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381768.

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Iyer, Aurobind V., Viral Pasad, Smita R. Sankhe, and Karan Prajapati. "Emotion based mood enhancing music recommendation." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256863.

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Przybysz, Andre L., Ricardo Corassa, Carolina L. Dos Santos, and Carlos N. Silla. "Latin Music Mood Classification Using Cifras." In 2015 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.296.

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Xiao, Zhongzhe, Di Wu, Xiaojun Zhang, and Zhi Tao. "Music mood tracking based on HCS." In 2012 11th International Conference on Signal Processing (ICSP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icosp.2012.6491785.

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Feng, Yazhong, Yueting Zhuang, and Yunhe Pan. "Popular music retrieval by detecting mood." In the 26th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/860435.860508.

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Laurier, Cyril, Owen Meyers, Joan Serra, Martin Blech, and Perfecto Herrera. "Music Mood Annotator Design and Integration." In 2009 Seventh International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2009. http://dx.doi.org/10.1109/cbmi.2009.45.

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Grunberg, David K., Alyssa M. Batula, Erik M. Schmidt, and Youngmoo E. Kim. "Affective gesturing with music mood recognition." In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). IEEE, 2012. http://dx.doi.org/10.1109/humanoids.2012.6651542.

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Seungjae Lee, Jung Hyun Kim, Sung Min Kim, and Won Young Yoo. "Smoodi: Mood-based music recommendation player." In 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6012116.

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Rho, Seungmin, Byeong-jun Han, and Eenjun Hwang. "SVR-based music mood classification and context-based music recommendation." In the seventeen ACM international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631272.1631395.

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Reports on the topic "Music and mood"

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Ding, Jie, Jinyu Hu, Fuqiang Yuan, and Yuanyi Xiao. Effectiveness and safety of music-supported therapy on mood in post-stroke rehabilitation patients: A systematic review and meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2021. http://dx.doi.org/10.37766/inplasy2021.2.0011.

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