Bibliographies thématiques / Music information processing

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Littérature scientifique sur le sujet « Music information processing »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Music information processing"

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Zhao, Tian, and Patricia K. Kuhl. "Music, speech, and temporal information processing." Journal of the Acoustical Society of America 144, no. 3 (2018): 1760. http://dx.doi.org/10.1121/1.5067789.

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Goto, Masataka, and Keiji Hirata. "Recent studies on music information processing." Acoustical Science and Technology 25, no. 6 (2004): 419–25. http://dx.doi.org/10.1250/ast.25.419.

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Tsuboi, Kuniharu. "Computer music and musical information processing." Journal of the Institute of Television Engineers of Japan 42, no. 1 (1988): 49–55. http://dx.doi.org/10.3169/itej1978.42.49.

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Katayose, Haruhiro. "The Dawn of Kansei Information Processing. Application of Kansei Information Processing. Music Performance." Journal of the Institute of Image Information and Television Engineers 52, no. 1 (1998): 53–55. http://dx.doi.org/10.3169/itej.52.53.

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Bugos, Jennifer, and Wendy Mostafa. "Musical Training Enhances Information Processing Speed." Bulletin of the Council for Research in Music Education, no. 187 (January 1, 2011): 7–18. http://dx.doi.org/10.2307/41162320.

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Abstract The purpose of this research is to examine the effects of music instruction on information processing speed. We examined music’s role on information processing speed in musicians (N = 14) and non-musicians (N = 16) using standardized neuropsychological measures, the Paced Auditory Serial Addition Task (PASAT) and the Trail Making Test (TMT). Results of a One Way ANOVA indicate significantly (p < .05) enhanced performance by musicians compared to non-musicians on the PASAT and TMT (Part A and B). These results suggest that musical training has the capacity to enhance processing
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Atherton, Ryan P., Quin M. Chrobak, Frances H. Rauscher, et al. "Shared Processing of Language and Music." Experimental Psychology 65, no. 1 (2018): 40–48. http://dx.doi.org/10.1027/1618-3169/a000388.

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Abstract. The present study sought to explore whether musical information is processed by the phonological loop component of the working memory model of immediate memory. Original instantiations of this model primarily focused on the processing of linguistic information. However, the model was less clear about how acoustic information lacking phonological qualities is actively processed. Although previous research has generally supported shared processing of phonological and musical information, these studies were limited as a result of a number of methodological concerns (e.g., the use of sim
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FUKAYAMA, Satoru. "Music Information Processing for Visualization with Musical Notations." Journal of the Visualization Society of Japan 40, no. 158 (2020): 19–22. http://dx.doi.org/10.3154/jvs.40.158_19.

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Rammsayer, Thomas, and Eckart Altenmüller. "Temporal Information Processing in Musicians and Nonmusicians." Music Perception 24, no. 1 (2006): 37–48. http://dx.doi.org/10.1525/mp.2006.24.1.37.

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The present study was designed to examine the general notion that temporal information processing is more accurate in musicians than in nonmusicians. For this purpose, 36 academically trained musicians and 36 nonmusicians performed seven different auditory temporal tasks. Superior temporal acuity for musicians compared to nonmusicians was shown for auditory fusion, rhythm perception, and three temporal discrimination tasks. The two groups did not differ, however, in terms of their performance on two tasks of temporal generalization. Musicians’superior performance appeared to be limited to aspe
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Li, Yi. "Digital Development for Music Appreciation of Information Resources Using Big Data Environment." Mobile Information Systems 2022 (September 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/7873636.

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With the continuous development of information technology and the arrival of the era of big data, music appreciation has also entered the digital development. Big data essence is highlighted by comparison with traditional data management and processing technologies. Under different requirements, the required time processing range is different. Music appreciation is an essential and important part of music lessons, which can enrich people’s emotional experience, improve aesthetic ability, and cultivate noble sentiments. Data processing of music information resources will greatly facilitate the
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Moreno, Alberto. "ELEMENTS OF MUSIC BASED ON ARTIFICIAL INTELLIGENCE." Acta Informatica Malaysia 4, no. 2 (2020): 30–32. http://dx.doi.org/10.26480/aim.02.2020.30.32.

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Thus, for the current status of research and practical music audio processing needs, this paper argues, the music element analysis technology is the key to this research field, and on this basis, proposes a new framework music processing – Music calculation system, the core objective is to study intelligently and automatically identifies various elements of music information and analyze the information used in constructing the music content, and intelligent retrieval method translated. To achieve the above core research objectives, the paper advocates will be closely integrated music theory an
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Thèses sur le sujet "Music information processing"

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Khadkevich, Maksim. "Music signal processing for automatic extraction of harmonic and rhythmic information." Doctoral thesis, Università degli studi di Trento, 2011. https://hdl.handle.net/11572/367673.

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This thesis is concerned with the problem of automatic extraction of harmonic and rhythmic information from music audio signals using statistical framework and advanced signal processing methods. Among different research directions, automatic extraction of chords and key has always been of a great interest to Music Information Retrieval (MIR) community. Chord progressions and key information can serve as a robust mid-level representation for a variety of MIR tasks. We propose statistical approaches to automatic extraction of chord progressions using Hidden Markov Models (HMM) based framewor
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Khadkevich, Maksim. "Music signal processing for automatic extraction of harmonic and rhythmic information." Doctoral thesis, University of Trento, 2011. http://eprints-phd.biblio.unitn.it/684/1/Maksim_Khadkevich_PhD_Thesis.pdf.

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This thesis is concerned with the problem of automatic extraction of harmonic and rhythmic information from music audio signals using statistical framework and advanced signal processing methods. Among different research directions, automatic extraction of chords and key has always been of a great interest to Music Information Retrieval (MIR) community. Chord progressions and key information can serve as a robust mid-level representation for a variety of MIR tasks. We propose statistical approaches to automatic extraction of chord progressions using Hidden Markov Models (HMM) based frame
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Al-Shakarchi, Ahmad. "Scalable audio processing across heterogeneous distributed resources : an investigation into distributed audio processing for Music Information Retrieval." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/47855/.

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Audio analysis algorithms and frameworks for Music Information Retrieval (MIR) are expanding rapidly, providing new ways to discover non-trivial information from audio sources, beyond that which can be ascertained from unreliable metadata such as ID3 tags. MIR is a broad field and many aspects of the algorithms and analysis components that are used are more accurate given a larger dataset for analysis, and often require extensive computational resources. This thesis investigates if, through the use of modern distributed computing techniques, it is possible to design an MIR system that is scala
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Suyoto, Iman S. H., and ishs@ishs net. "Cross-Domain Content-Based Retrieval of Audio Music through Transcription." RMIT University. Computer Science and Information Technology, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090527.092841.

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Research in the field of music information retrieval (MIR) is concerned with methods to effectively retrieve a piece of music based on a user's query. An important goal in MIR research is the ability to successfully retrieve music stored as recorded audio using note-based queries. In this work, we consider the searching of musical audio using symbolic queries. We first examined the effectiveness of using a relative pitch approach to represent queries and pieces. Our experimental results revealed that this technique, while effective, is optimal when the whole tune is used as a query. We th
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Byron, Timothy Patrick. "The processing of pitch and temporal information in relational memory for melodies." View thesis, 2008. http://handle.uws.edu.au:8081/1959.7/37492.

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Thesis (Ph.D.) -- University of Western Sydney, 2008.<br>A thesis submitted to the University of Western Sydney, College of Arts, School of Psychology, in fulfilment of the requirements for the degree of Doctor of Philosophy. Includes bibliographical references.
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Meinz, Elizabeth J. "Musical experience, musical knowledge and age effects on memory for music." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30881.

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Montecchio, Nicola. "Alignment and Identification of Multimedia Data: Application to Music and Gesture Processing." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422091.

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The overwhelming availability of large multimedia collections poses increasingly challenging research problems regarding the organization of, and access to data. A general consensus has been reached in the Information Retrieval community, asserting the need for tools that move past metadata-based techniques and exploit directly the information contained in the media. At the same time, interaction with content has evolved beyond the traditional passive enjoyment paradigm, bringing forth the demand for advanced control and manipulation options. The aim of this thesis is to investigate techniq
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Sanden, Christopher, and University of Lethbridge Faculty of Arts and Science. "An empirical evaluation of computational and perceptual multi-label genre classification on music / Christopher Sanden." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2602.

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Automatic music genre classi cation is a high-level task in the eld of Music Information Retrieval (MIR). It refers to the process of automatically assigning genre labels to music for various tasks, including, but not limited to categorization, organization and browsing. This is a topic which has seen an increase in interest recently as one of the cornerstones of MIR. However, due to the subjective and ambiguous nature of music, traditional single-label classi cation is inadequate. In this thesis, we study multi-label music genre classi cation from perceptual and computational perspectives. F
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Fiebrink, Rebecca. "An exploration of feature selection as a tool for optimizing musical genre classification /." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99372.

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The computer classification of musical audio can form the basis for systems that allow new ways of interacting with digital music collections. Existing music classification systems suffer, however, from inaccuracy as well as poor scalability. Feature selection is a machine-learning tool that can potentially improve both accuracy and scalability of classification. Unfortunately, there is no consensus on which feature selection algorithms are most appropriate or on how to evaluate the effectiveness of feature selection. Based on relevant literature in music information retrieval (MIR) and machin
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Bianchi, Frederick W. "The cognition of atonal pitch structures." Virtual Press, 1985. http://liblink.bsu.edu/uhtbin/catkey/438705.

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The Cognition of Atonal Pitch Structures investigated the ability of a listener to internally organize atonal pitch sequences into hierarchical structures. Based on an information processing model proposed by Deutsch and Feroe (1981), the internal organization of well processed pitch sequences will result in the formation of hierarchical structures. The more efficiently information is processed by the listener, the more organized its internal hierarchical representation in memory. Characteristic of a well organized internal hierarchy As redundancy. Each ensuing level of the hierarchical struct
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Livres sur le sujet "Music information processing"

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Müller, Meinard. Information retrieval for music and motion. Springer, 2007.

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H, Chen Homer, ed. Music emotion recognition. CRC, 2011.

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Taraeva, G. R., and T. F. Shak. Muzyka v informat︠s︡ionnom mire: Nauka, tvorchestvo, pedagogika : sbornik nauchnykh stateĭ = Music in the world of information : science, creative work, pedagogics : collection of articles. [Izd-vo Rostovskoĭ gos. konservatorii], 2004.

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International Conference on Music Information Retrieval (7th 2006 Victoria, British Columbia, Canada). ISMIR 2006: 7th International Conference on Music Information Retrieval : Proceedings : Fairmont Empress Hotel, Victoria, BC, Canada, 8-12 October, 2006. Published by University of Victoria, 2006.

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1943-, Fingerhut Michael, and IRCAM (Research institute : France), eds. ISMIR 2002 conference proceedings: Third International Conference on Music Information Retrieval, October 13-17, 2002, IRCAM-Centre Pompidou, Paris, France. IRCAM-Centre Pompidou, 2002.

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Celma, Òscar. Music recommendation and discovery: The long tail, long fail, and long play in the digital music space. Springer, 2010.

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Computer Music Modeling and Retrieval Symposium (10th 2010 Malaga, Spain). Exploring music contents: 7th International Symposium, CMMR 2010, Malaga, Spain, June 21-24, 2010 : revised papers. Springer, 2011.

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Ystad, Sølvi. Speech, Sound and Music Processing: Embracing Research in India: 8th International Symposium, CMMR 2011, 20th International Symposium, FRSM 2011, Bhubaneswar, India, March 9-12, 2011, Revised Selected Papers. Springer Berlin Heidelberg, 2012.

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David, Hutchison. Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music: 5th International Symposium, CMMR 2008 Copenhagen, Denmark, May 19-23, 2008 Revised Papers. Springer Berlin Heidelberg, 2009.

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Haus, Goffredo, and Denis Baggi. Music navigation with symbols and layers: Toward content browsing with IEEE 1599 XML encoding. John Wiley & Sons, 2012.

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Chapitres de livres sur le sujet "Music information processing"

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Baras, C., N. Moreau, and T. Dutoit. "How could music contain hidden information?" In Applied Signal Processing. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-74535-0_7.

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Medhat, Fady, David Chesmore, and John Robinson. "Music Genre Classification Using Masked Conditional Neural Networks." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_49.

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Zhong, Guoqiang, Haizhen Wang, and Wencong Jiao. "MusicCNNs: A New Benchmark on Content-Based Music Recommendation." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_36.

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Fang, Qianqi, Ling Liu, Junliang Yu, and Junhao Wen. "Meta-path Based Heterogeneous Graph Embedding for Music Recommendation." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_10.

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Monsignori, M., P. Nesi, and M. B. Spinu. "Watermarking Music Sheets." In Advances in Multimedia Information Processing — PCM 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45453-5_83.

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Sitarek, Tomasz, and Wladyslaw Homenda. "Efficient Processing the Braille Music Notation." In Computer Information Systems and Industrial Management. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33260-9_29.

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Ikeuchi, Ryota, and Kazushi Ikeda. "An Automatic Music Transcription Based on Translation of Spectrum and Sound Path Estimation." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_64.

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Brewer, Madeline, and Jessica Sharmin Rahman. "Pruning Long Short Term Memory Networks and Convolutional Neural Networks for Music Emotion Recognition." In Neural Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_29.

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Duan, Ruo-Nan, Xiao-Wei Wang, and Bao-Liang Lu. "EEG-Based Emotion Recognition in Listening Music by Using Support Vector Machine and Linear Dynamic System." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34478-7_57.

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Liu, Ning-Han, and Shu-Ju Hsieh. "Intelligent Music Playlist Recommendation Based on User Daily Behavior and Music Content." In Advances in Multimedia Information Processing - PCM 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_59.

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Actes de conférences sur le sujet "Music information processing"

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Wu, Junda, Zachary Novack, Amit Namburi, et al. "FUTGA-MIR: Enhancing Fine-grained and Temporally-aware Music Understanding with Music Information Retrieval." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888485.

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Chen, Jing, Weixiang Gao, and Kai Zhang. "A CNN-Based Optical Music Recognition Method." In 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2024. https://doi.org/10.1109/cipae64326.2024.00056.

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Guinot, Julien, Elio Quinton, and György Fazekas. "Leave-One-EquiVariant: Alleviating Invariance-Related Information Loss in Contrastive Music Representations." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890270.

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Wang, Yue. "Research on Algorithm Based Music Creation and Automatic Composition System." In 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2024. https://doi.org/10.1109/cipae64326.2024.00137.

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Zhang, Shaoxiang, Peng Lin, Yongchang Ma, and Li Xie. "An Attention Based CNN-LSTM Hybrid Approach for Music Genre Classification." In 2024 7th International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2024. https://doi.org/10.1109/icicsp62589.2024.10809153.

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Berger, Markus, and Joern Ploennigs. "Ambisynth – Monitoring Room Climate with Generative Modular Synthesizers." In ICAD 2024: The 29th International Conference on Auditory Display. International Community for Auditory Display, 2024. http://dx.doi.org/10.21785/icad2024.023.

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When engaged in focused work, it can be easy to forget about ambient comfort factors, like high CO2 concentrations or bad lighting conditions. Ambient Information Systems can help us passively monitor such parameters, but often have the downside of intrusive notifcation sounds or requiring visual feedback. In this paper, we propose to subtly communicate sensor information as part of generative ambient background music. This genre has recently been popularized through modular hardware synthesizers, which create music by processing analog voltage signals. We combine these modules with analog roo
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Bozkurt, Baris, Ali Cenk Gedik, and M. Kemal Karaosmanoglu. "Music information retrieval for Turkish music: problems, solutions and tools." In 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136518.

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Simonetta, Federico, Stavros Ntalampiras, and Federico Avanzini. "Multimodal Music Information Processing and Retrieval: Survey and Future Challenges." In 2019 International Workshop on Multilayer Music Representation and Processing (MMRP). IEEE, 2019. http://dx.doi.org/10.1109/mmrp.2019.00012.

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Simonetta, Federico, Stavros Ntalampiras, and Federico Avanzini. "Multimodal Music Information Processing and Retrieval: Survey and Future Challenges." In 2019 International Workshop on Multilayer Music Representation and Processing (MMRP). IEEE, 2019. http://dx.doi.org/10.1109/mmrp.2019.8665366.

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Wang, Tao, Dong-Ju Kim, Kwang-Seok Hong, and Jeh-Seon Youn. "Music Information Retrieval System Using Lyrics and Melody Information." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.283.

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Rapports d'organisations sur le sujet "Music information processing"

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Sun, He. Animated electronic storybook and children's Mother Tongue development: Tracing the process and the outcome with eye-tracking. National Institute of Education, Nanyang Technological University, Singapore, 2020. https://doi.org/10.32658/10497/22664.

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Storybook reading to children is considered an efficient way to provide a meaningful context for exposure to unfamiliar words and grammar (Weizman &amp; Snow, 2001). Nonetheless, children with limited language knowledge (e.g., child MTL learners in Singapore) may benefit less from the reading activities, due to the gap between their skills and those required for processing the narration. They often fail to derive the meaning of unknown words/grammar from the verbal context and consequently have trouble figuring out the story plots (Verhallen &amp; Bus, 2010). Children’s electronic storybook (e
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