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Relevant bibliographies by topics / Musical intelligence

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Academic literature on the topic 'Musical intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Musical intelligence"

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Moreno, Joseph. "Essential Musical Intelligence." Arts in Psychotherapy 30, no. 4 (January 2003): 241–42. http://dx.doi.org/10.1016/s0197-4556(03)00054-6.

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Santiago-Arreola, Keren Sarahí, and Carlos Eduardo García-Hernández. "Inteligencia musical para todos/as." RA RIÓ GUENDARUYUBI 2, no. 6 (May 20, 2019): 36–43. http://dx.doi.org/10.53331/rar.v2i6.1098.

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This paper takes up musical intelligence as an inherent element of the human being, the advantages of its development in the formative processes and its role in everyday life. Thus, this paper goes beyond the old notion of intelligence measured by test, arriving to the theory of multiple intelligences raised by Gardner, in which cognition is constructed from various attributes and not only from those grouped in the tests.

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Rahmalia, Indah. "students' Multiple Intelligence in Learning during TOEFL Preparation." TELL-US JOURNAL 2, no. 2 (March 17, 2017): 42–55. http://dx.doi.org/10.22202/tus.2016.v2i2.1386.

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The purpose of this study was to find out students’ Multiple intelligece in learning during TOEFL preparation at STKIP Abdi Pendidikan Payakumbuh. This study was carried out of descriptive research that undertaken through phenomenological approach by employing questionnaire to 31 students selected by using purposive sampling technique. The findings of this study showed that the most multiple intelligence used by the students was musical intelligence with the score 606 , while the multiple intelligences that less dominant used by the students was logical-mathematical intelligence with the score 531.

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Gholam-Shahbazi, Hassti. "The Relationship between Spatial and Musical Intelligences and EFL Learners’ Learning Styles and Vocabulary Knowledge." Journal of Language Teaching and Research 10, no. 4 (July 1, 2019): 747. http://dx.doi.org/10.17507/jltr.1004.09.

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This study investigated the relationship between spatial and musical intelligences and learning styles of EFL learners and their vocabulary knowledge. Accordingly, relationship between spatial intelligence, musical intelligence and vocabulary knowledge, visual learning style, auditory learning style, and kinesthetic learning style with vocabulary knowledge, listening and vocabulary knowledge, and finally spatial, musical intelligence, visual, auditory kinesthetic learning style as independent variables and vocabulary knowledge s dependent variable was examined. This study is an experimental and applied research using four texts to specify participants intelligence their learning styles, vocabulary knowledge and listening knowledge. For this reason, four texts including MIDAS Test of Shearer (1996) the Persian of Spatial, and Musical Intelligence VAK Learning Style Test (Visual, Auditory, Kinesthetic), Vocabulary Levels Test of Nation (2001) One listening Test from the IELTS 5 book were applied. In this study, 200 Iranian senior BA EFL learners from Islamic Azad Universities of Tehran, Male and Female, 22 to 30 years old, majoring in TEFL were examined. Result of data analysis showed that there is a significant relationship between spatial and musical intelligences and learning styles of Iranian EFL learners and their vocabulary knowledge. Also, multiple intelligence plays a significant role in learning vocabulary, as the nature of intelligence represents this issue and shows that learning is a psychological issue and human’s different aspects of learning depends of different aspect of intelligence.

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Kostka, Stefan, and David Cope. "Experiments in Musical Intelligence." Notes 54, no. 1 (September 1997): 98. http://dx.doi.org/10.2307/899968.

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Putnam, Karl W., and David Cope. "Experiments in Musical Intelligence." Computer Music Journal 21, no. 3 (1997): 102. http://dx.doi.org/10.2307/3681022.

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Matare, Joseph. "Creativity or musical intelligence?" Thinking Skills and Creativity 4, no. 3 (December 2009): 194–203. http://dx.doi.org/10.1016/j.tsc.2009.09.005.

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Hermelin, B., N. O'Connor, S. Lee, and D. Treffert. "Intelligence and musical improvisation." Psychological Medicine 19, no. 2 (May 1989): 447–57. http://dx.doi.org/10.1017/s0033291700012484.

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SynopsisWe investigated whether somebody with a severe mental impairment could not only remember and reproduce music, but was also able to generate it. Musical improvisation requires the ability to recognize constraints and also demands inventiveness.Musical improvisations on a traditional, tonal and also on a whole tone scale composition were produced by a mentally handicapped and by a normal control musician. It was found that not only the control but also the handicapped subject could improvise appropriately within structural constraints, although with the tonal music the idiot-savant showed some stylistic latitude. It is concluded that cognitive processes such as musical input analysis, decision making, and output monitoring are independent of general intellectual status.

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Rahmawati, Laila Eka, Linda Mayasari, and Sulton Dedi Wijaya. "MULTIPLE INTELLIGENCES PROFILE OF EFL STUDENTS AT SMP MUHAMMADIYAH 15 SURABAYA." Scientia: Jurnal Hasil Penelitian 5, no. 1 (August 8, 2020): 16–20. http://dx.doi.org/10.32923/sci.v5i1.1334.

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This research analyzes the multiple intelligences in English Foreign Language (EFL), especially in 8A grade students. It identifies the multiple intelligences profile based on the frequency of multiple intelligence (MI) types, students’ high scores, and low scores. This research uses descriptive qualitative design. The subject of this research was 8A grade students in SMP Muhammadiyah 15 Surabaya. The instruments of the research were Multiple Intelligences Questionnaires, IQ results, and documentation of English mid-test scores in the second semester. From analysis and discussion, it was found that this class’s IQ score average is 86, and the most dominant intelligence type is musical. For the five highest score students’ there were four types of intelligence that they had. They were interpersonal, naturalist, logical-mathematical, and musical. Meanwhile, five low English achievers had four types of multiple intelligence namely were musical, interpersonal, naturalist, and visual-spatial. In conclusion, the class has diversity in multiple intelligence. Multiple Intelligence types cannot refer to a certain group, in this case, groups based on their English score, because some MI types can be found in the high and low English achiever groups.

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Reinhard, Johnny. "Howard Gardner’s MULTIPLE INTELLIGENCES: A Minority View: Musical Intelligence." Music Scholarship / Problemy Muzykal'noj Nauki, no. 4 (2015): 38–42. http://dx.doi.org/10.17674/1997-0854.2015.4.038-042.

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Dissertations / Theses on the topic "Musical intelligence"

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Matare, Joseph. "The nature of musical intelligence / musical abilities." Thesis, University of Exeter, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407292.

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Kendra, Melanie A. "An Exploration of Musical Intelligence." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/32840.

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This study explored the meaning teachers and children in an elementary school setting make of music. This study utilized an adapted version of Thomas Armstrongâ s musical intelligence checklist, as well as additional questions probing both teachers and children to relate their past and present experiences with music and how they are musically intelligent. This study was conducted with two third grade classes and two fifth grade classes, with the researcher spending one class period with each group. Implications for how music is affecting elementary students, as well as the meaning it holds for teachers, were discussed. In addition, recommendations for future study of music in early education were made.
Master of Science

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Hutson, Matt 1978. "Artificial intelligence and musical creativity : computing Beethoven's tenth." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/85756.

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Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Humanities, Program in Writing and Humanistic Studies, 2003.
Includes bibliographical references (p. 47-48).
by Matthew T. Hutson.
S.M.in Science Writing

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Howard, Sara Louise. "Exceptional musical performance : assessment of intelligence, musical aptitude, practice and empathy as contributing factors /." Title page, table of contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09HS/09hsh8481.pdf.

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Michels, Patricia. "The role of the musical intelligence in whole brain education." Diss., Pretoria : [s.n.], 2001. http://upetd.up.ac.za/thesis/available/etd-06142002-125955.

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Curry, Benjamin David. "Towards a computational model of musical accompaniment : disambiguation of musical analyses by reference to performance data." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/809.

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A goal of Artificial Intelligence is to develop computational models of what would be considered intelligent behaviour in a human. One such task is that of musical performance. This research specifically focuses on aspects of performance related to the performance of musical duets. We present the research in the context of developing a cooperative performance system that would be capable of performing a piece of music expressively alongside a human musician. In particular, we concentrate on the relationship between musical structure and performance with the aim of creating a structural interpretation of a piece of music by analysing features of the score and performance. We provide a new implementation of Lerdahl and Jackendoff’s Grouping Structure analysis which makes use of feature-category weighting factors. The multiple structures that result from this analysis are represented using a new technique for representing hierarchical structures. The representation supports a refinement process which allows the structures to be disambiguated at a later stage. We also present a novel analysis technique, based on the principle of phrase-final lengthening, to identify structural features from performance data. These structural features are used to select from the multiple possible musical structures the structure that corresponds most closely to the analysed performance. The three main contributions of this research are:1- An implementation of Lerdahl and Jackendoff’s Grouping Structure which includes feature-category weighting factors; 2- A method of storing a set of ambiguous hierarchical structures which supports gradual improvements in specificity; An analysis technique which, when applied to a musical performance, succeeds 3- in providing information to aid the disambiguation of the final musical structure. The results indicate that the approach has promise and with the incorporation of further refinements could lead to a computer-based system that could aid both musical performers and those interested in the art of musical performance.

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Michels, Patricia E. "Developing the pre-school child's musical intelligence by means of a comprehensive music programme focused on age-controlled auditive development." Pretoria : [s.n.], 1996. http://upetd.up.ac.za/thesis/available/etd-07262002-151835.

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Phillips, Justin M. "Functional Reactive Musical Performers." DigitalCommons@CalPoly, 2010. https://digitalcommons.calpoly.edu/theses/410.

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Computers have been assisting in recording, sound synthesis and other fields of music production for quite some time. The actual performance of music continues to be an area in which human players are chosen over computer performers. Musical performance is an area in which personalization is more important than consistency. Human players play with each other, reacting to phrases and ideas created by the players that they are playing with. Computer performers lack the ability to react to the changes in the performance that humans perceive naturally, giving the human players an advantage over the computer performers. This thesis creates a framework for describing unique musical performers that can play along in realtime with human players. FrTime, a reactive programming language, is used to constantly create new musical phrases. Musical phrases are constructed by unique user programmed performers and by chord changes that the framework provides. The reactive language creates multiple musical phrases for each point in time. A simple module which chooses musical phrases to be performed at the time of performance is created.

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Albin, Aaron Thomas. "Musical swarm robot simulation strategies." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42862.

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Swarm robotics for music is a relatively new way to explore algorithmic composition as well as new modes of human robot interaction. This work outlines a strategy for making music with a robotic swarm constrained by acoustic sound, rhythmic music using sequencers, motion causing changes in the music, and finally human and swarm interaction. Two novel simulation programs are created in this thesis: the first is a multi-agent simulation designed to explore suitable parameters for motion to music mappings as well as parameters for real time interaction. The second is a boid-based robotic swarm simulation that adheres to the constraints established, using derived parameters from the multi-agent simulation: orientation, number of neighbors, and speed. In addition, five interaction modes are created that vary along an axis of direct and indirect forms of human control over the swarm motion. The mappings and interaction modes of the swarm robot simulation are evaluated in a user study involving music technology students. The purpose of the study is to determine the legibility of the motion to musical mappings and evaluate user preferences for the mappings and modes of interaction in problem solving and in open-ended contexts. The findings suggest that typical users of a swarm robot system do not necessarily prefer more inherently legible mappings in open-ended contexts. Users prefer direct and intermediate modes of interaction in problem solving scenarios, but favor intermediate modes of interaction in open-ended ones. The results from this study will be used in the design and development of a new swarm robotic system for music that can be used in both contexts.

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Pounds, Michael S. "Using spatial analogy to determine musical parameters in algorithmic composition." Virtual Press, 1995. http://liblink.bsu.edu/uhtbin/catkey/958778.

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This thesis presents a method of algorithmic composition in which the music is seen as motion through a multidimensional musical space. An analogy is drawn between physical space and musical space, each direction of the physical space corresponding to a musical parameter. A computer program was developed using the MAX programming environment to simulate the goaldirected motion of a mobile robot through an environment containing obstacles. The potential field method of mobile robot path planning was used. The program maps the location of the robot to musical parameters in the musical space. Based on the instantaneous values of the musical parameters, the program generates melodic material and transmits the resulting MIDI data to a synthesizer. For this research, the program was limited to three spatial dimensions and one obstacle. The program successfully created simple compositions consisting of large musical gestures. A model composition was created. Suggestions were made for further development and more elaborate applications of the method.
School of Music

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Books on the topic "Musical intelligence"

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Cope, David. Experiments in musical intelligence. Madison, Wis: A-R Editions, 1996.

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Cope, David. Experiments in musical intelligence. Madison, Wis: A-R Editions, 1996.

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Liebman, Elad. Sequential Decision-Making in Musical Intelligence. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30519-2.

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The mind behind the musical ear: How children develop musical intelligence. Cambridge, Mass: Harvard University Press, 1991.

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Kia, Ng, and SpringerLink (Online service), eds. Musical Robots and Interactive Multimodal Systems. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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McAnulty, Heather M. E. Correlation between musical ability and intelligence after correcting for attenuation. [s.l: The Author], 1988.

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Desain, Peter. Music, mind, and machine: Studies in computer music, music cognition, and artificial intelligence. Amsterdam: Thesis, 1992.

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Swanner, Diana Lee. Relationships between musical creativity and selected factors, including personality, motivation, musical aptitude, and cognitive intelligence as measured in third grade children. [Cleveland, Ohio: s.n.], 1985.

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Artificial perception and music recognition. Berlin: Springer-Verlag, 1993.

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Stokes, W. Ann. Intelligence and feeling: A philosophical examination of these concepts as interdependent factors in musical experience and music education. Evanston, Ill: [s.n.], 1990.

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Book chapters on the topic "Musical intelligence"

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Sadiku, Matthew N. O., and Sarhan M. Musa. "Musical Intelligence." In A Primer on Multiple Intelligences, 237–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77584-1_19.

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Che, Rui, Xingda Li, Dongfang Li, and Yujing Guan. "Musical Intelligence Analysis." In Recent Advances in Computer Science and Information Engineering, 671–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25792-6_102.

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Lindemann, Anna, and Eric Lindemann. "Musical Organisms." In Computational Intelligence in Music, Sound, Art and Design, 128–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77583-8_9.

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Challis, Ben. "Designing for Musical Play." In Studies in Computational Intelligence, 197–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45432-5_10.

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Cosentino, Sarah, and Atsuo Takanishi. "Human–Robot Musical Interaction." In Handbook of Artificial Intelligence for Music, 799–822. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72116-9_28.

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Loui, Psyche. "Neuroscience of Musical Improvisation." In Handbook of Artificial Intelligence for Music, 97–115. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72116-9_5.

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Wieczorkowska, Alicja A., and Zbigniew W. Raś. "Do We Need Automatic Indexing of Musical Instruments?" In Intelligent Media Technology for Communicative Intelligence, 239–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11558637_24.

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Igarashi, Soh, Tomonobu Ozaki, and Koichi Furukawa. "Respiration Reflecting Musical Expression: Analysis of Respiration during Musical Performance by Inductive Logic Programming." In Music and Artificial Intelligence, 94–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45722-4_10.

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Dannenberg, Roger B., and Ning Hu. "Discovering Musical Structure in Audio Recordings." In Music and Artificial Intelligence, 43–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45722-4_6.

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Braga, Francisco, and H. Sofia Pinto. "Sculpture Inspired Musical Composition." In Artificial Intelligence in Music, Sound, Art and Design, 3–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72914-1_1.

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Conference papers on the topic "Musical intelligence"

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Hung, Yun-Ning, I.-Tung Chiang, Yi-An Chen, and Yi-Hsuan Yang. "Musical Composition Style Transfer via Disentangled Timbre Representations." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/652.

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Music creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. While the former has received increasing attention, the latter has not been much investigated. This paper presents, to the best of our knowledge, the first deep learning models for rearranging music of arbitrary genres. Specifically, we build encoders and decoders that take a piece of polyphonic musical audio as input, and predict as output its musical score. We investigate disentanglement techniques such as adversarial training to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation (timbre) of the parts per short-time segment. By disentangling pitch and timbre, our models have an idea of how each piece was composed and arranged. Moreover, the models can realize “composition style transfer” by rearranging a musical piece without much affecting its pitch content. We validate the effectiveness of the models by experiments on instrument activity detection and composition style transfer. To facilitate follow-up research, we open source our code at https://github.com/biboamy/instrument-disentangle.

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Catania, Fabio, Giorgio De Luca, Nicola Bombaci, Erica Colombo, Pietro Crovari, Eleonora Beccaluva, and Franca Garzotto. "Musical and Conversational Artificial Intelligence." In IUI '20: 25th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3379336.3381479.

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Cornelissen, Willy, and Maurício Loureiro. "Automatic onset detection using convolutional neural networks." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10446.

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A very significant task for music research is to estimate instants when meaningful events begin (onset) and when they end (offset). Onset detection is widely applied in many fields: electrocardiograms, seismographic data, stock market results and many Music Information Research(MIR) tasks, such as Automatic Music Transcription, Rhythm Detection, Speech Recognition, etc. Automatic Onset Detection(AOD) received, recently, a huge contribution coming from Artificial Intelligence (AI) methods, mainly Machine Learning and Deep Learning. In this work, the use of Convolutional Neural Networks (CNN) is explored by adapting its original architecture in order to apply the approach to automatic onset detection on audio musical signals. We used a CNN network for onset detection on a very general dataset, well acknowledged by the MIR community, and examined the accuracy of the method by comparison to ground truth data published by the dataset. The results are promising and outperform another methods of musical onset detection.

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Guan Yin and Zhou Changle. "Music rhythm tagging: New applications for music information retrieval and musical intelligence." In 2012 IEEE International Conference on Oxide Materials for Electronic Engineering (OMEE). IEEE, 2012. http://dx.doi.org/10.1109/omee.2012.6343688.

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Schimbinschi, Florin, Christian Walder, Sarah M. Erfani, and James Bailey. "SynthNet: Learning to Synthesize Music End-to-End." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/467.

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We consider the problem of learning a mapping directly from annotated music to waveforms, bypassing traditional single note synthesis. We propose a specific architecture based on WaveNet, a convolutional autoregressive generative model designed for text to speech. We investigate the representations learned by these models on music and concludethat mappings between musical notes and the instrument timbre can be learned directly from the raw audio coupled with the musical score, in binary piano roll format.Our model requires minimal training data (9 minutes), is substantially better in quality and converges 6 times faster in comparison to strong baselines in the form of powerful text to speech models.The quality of the generated waveforms (generation accuracy) is sufficiently high,that they are almost identical to the ground truth.Our evaluations are based on both the RMSE of the Constant-Q transform, and mean opinion scores from human subjects.We validate our work using 7 distinct synthetic instrument timbres, real cello music and also provide visualizations and links to all generated audio.

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Chen, Yu-Hua, Bryan Wang, and Yi-Hsuan Yang. "Demonstration of PerformanceNet: A Convolutional Neural Network Model for Score-to-Audio Music Generation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/938.

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We present in this paper PerformacnceNet, a neural network model we proposed recently to achieve score-to-audio music generation. The model learns to convert a music piece from the symbolic domain to the audio domain, assigning performance-level attributes such as changes in velocity automatically to the music and then synthesizing the audio. The model is therefore not just a neural audio synthesizer, but an AI performer that learns to interpret a musical score in its own way. The code and sample outputs of the model can be found online at https://github.com/bwang514/PerformanceNet.

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Ginanjar, Rikip, and Ivan Iskandar. "MIDI Conversion to Musical Notation." In 2011 First International Conference on Informatics and Computational Intelligence (ICI). IEEE, 2011. http://dx.doi.org/10.1109/ici.2011.25.

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Vargas, Francisco Vilchez, Jose Astuvilca Fuster, and Cesar Beltran Castanon. "Artificial musical pattern generation with genetic algorithms." In 2015 Latin America Congress on Computational Intelligence (LA-CCI). IEEE, 2015. http://dx.doi.org/10.1109/la-cci.2015.7435956.

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Wang, Junhao, Cong Jin, Wei Zhao, Shan Liu, and Xin Lv. "An Unsupervised Methodology for Musical Style Translation." In 2019 15th International Conference on Computational Intelligence and Security (CIS). IEEE, 2019. http://dx.doi.org/10.1109/cis.2019.00053.

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Chathuranga, Dhanith, and Lakshman Jayaratne. "Musical Genre Classification Using Ensemble of Classifiers." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.47.

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Reports on the topic "Musical intelligence"

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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, October 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve" the necessary actions for the body in the external environment, for example, to take a cup of coffee or turn on your favorite music. At the same time, an artificial neural network that analyzes the flow of nerve impulses must also be directed at the brain, trying to guess the body's needs at the moment with a minimum number of movements. The most important obstacle to further progress is the problem of biocompatibility, which has not yet been resolved. This is even more important than the number of electrodes and the power of the processors on the chip. When you insert a foreign object into your brain, it tries to isolate itself from it. This is a multidisciplinary topic not only for doctors and psychophysiologists, but also for engineers, programmers, mathematicians. Of course, the problem is complex and it will be possible to overcome it only with joint efforts.

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Yatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.

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The article investigates functional techniques of extralinguistic expression in multimedia texts; the effectiveness of figurative expressions as a reaction to modern events in Ukraine and their influence on the formation of public opinion is shown. Publications of journalists, broadcasts of media resonators, experts, public figures, politicians, readers are analyzed. The language of the media plays a key role in shaping the worldview of the young political elite in the first place. The essence of each statement is a focused thought that reacts to events in the world or in one’s own country. The most popular platform for mass information and social interaction is, first of all, network journalism, which is characterized by mobility and unlimited time and space. Authors have complete freedom to express their views in direct language, including their own word formation. Phonetic, lexical, phraseological and stylistic means of speech create expression of the text. A figurative word, a good aphorism or proverb, a paraphrased expression, etc. enhance the effectiveness of a multimedia text. This is especially important for headlines that simultaneously inform and influence the views of millions of readers. Given the wide range of issues raised by the Internet as a medium, research in this area is interdisciplinary. The science of information, combining language and social communication, is at the forefront of global interactions. The Internet is an effective source of knowledge and a forum for free thought. Nonlinear texts (hypertexts) – «branching texts or texts that perform actions on request», multimedia texts change the principles of information collection, storage and dissemination, involving billions of readers in the discussion of global issues. Mastering the word is not an easy task if the author of the publication is not well-read, is not deep in the topic, does not know the psychology of the audience for which he writes. Therefore, the study of media broadcasting is an important component of the professional training of future journalists. The functions of the language of the media require the authors to make the right statements and convincing arguments in the text. Journalism education is not only knowledge of imperative and dispositive norms, but also apodictic ones. In practice, this means that there are rules in media creativity that are based on logical necessity. Apodicticity is the first sign of impressive language on the platform of print or electronic media. Social expression is a combination of creative abilities and linguistic competencies that a journalist realizes in his activity. Creative self-expression is realized in a set of many important factors in the media: the choice of topic, convincing arguments, logical presentation of ideas and deep philological education. Linguistic art, in contrast to painting, music, sculpture, accumulates all visual, auditory, tactile and empathic sensations in a universal sign – the word. The choice of the word for the reproduction of sensory and semantic meanings, its competent use in the appropriate context distinguishes the journalist-intellectual from other participants in forums, round tables, analytical or entertainment programs. Expressive speech in the media is a product of the intellect (ability to think) of all those who write on socio-political or economic topics. In the same plane with him – intelligence (awareness, prudence), the first sign of which (according to Ivan Ogienko) is a good knowledge of the language. Intellectual language is an important means of organizing a journalistic text. It, on the one hand, logically conveys the author’s thoughts, and on the other – encourages the reader to reflect and comprehend what is read. The richness of language is accumulated through continuous self-education and interesting communication. Studies of social expression as an important factor influencing the formation of public consciousness should open up new facets of rational and emotional media broadcasting; to trace physical and psychological reactions to communicative mimicry in the media. Speech mimicry as one of the methods of disguise is increasingly becoming a dangerous factor in manipulating the media. Mimicry is an unprincipled adaptation to the surrounding social conditions; one of the most famous examples of an animal characterized by mimicry (change of protective color and shape) is a chameleon. In a figurative sense, chameleons are called adaptive journalists. Observations show that mimicry in politics is to some extent a kind of game that, like every game, is always conditional and artificial.

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