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Journal articles on the topic 'Music information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Downie, J. Stephen. "Music information retrieval." Annual Review of Information Science and Technology 37, no. 1 (2005): 295–340. http://dx.doi.org/10.1002/aris.1440370108.

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2

Knees, Peter, Markus Schedl, and Òscar Celma. "Hybrid music information retrieval." International Journal of Multimedia Information Retrieval 2, no. 1 (2013): 1–2. http://dx.doi.org/10.1007/s13735-013-0033-9.

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3

Weissenberger, Lynnsey. "Toward a universal, meta-theoretical framework for music information classification and retrieval." Journal of Documentation 71, no. 5 (2015): 917–37. http://dx.doi.org/10.1108/jd-08-2013-0106.

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Purpose – The purpose of this paper is to present a new framework for representing music for information retrieval that emphasizes socio-cultural aspects of music. Design/methodology/approach – Philosophical and theoretical concepts related to the nature of music, aboutness, musical works are explored as they inform how music is represented. Multidisciplinary perspectives on music information representation, classification, and retrieval provide insight into how information science can better accommodate music information within its disciplinary boundaries. Findings – A new term, music informa

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4

OKUNO, Hiroshi G., Tetsuro KITAHARA, and Kazuyoshi YOSHII. "Music Feature Extraction and Music Information Retrieval." Journal of The Institute of Electrical Engineers of Japan 127, no. 7 (2007): 417–20. http://dx.doi.org/10.1541/ieejjournal.127.417.

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5

de Haas, W. Bas, and Frans Wiering. "Hooked on Music Information Retrieval." Empirical Musicology Review 5, no. 4 (2010): 176–85. http://dx.doi.org/10.18061/1811/48551.

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6

Tao Li and M. Ogihara. "Toward intelligent music information retrieval." IEEE Transactions on Multimedia 8, no. 3 (2006): 564–74. http://dx.doi.org/10.1109/tmm.2006.870730.

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7

Urbano, Julián, Markus Schedl, and Xavier Serra. "Evaluation in Music Information Retrieval." Journal of Intelligent Information Systems 41, no. 3 (2013): 345–69. http://dx.doi.org/10.1007/s10844-013-0249-4.

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8

Downie, J. Stephen. "The music information retrieval evaluation exchange (2005–2007): A window into music information retrieval research." Acoustical Science and Technology 29, no. 4 (2008): 247–55. http://dx.doi.org/10.1250/ast.29.247.

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9

Lee, Kuo Ying, and Chih Ming Hu. "Research on the Development of Music Information Retrieval and Fuzzy Search." Scientific and Social Research 4, no. 4 (2022): 1–10. http://dx.doi.org/10.26689/ssr.v4i4.3771.

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With the popularization of modern broadband networks, many network resources are serving as media for the public to seek knowledge. In order to help users avoid spending hours searching for music-related information, establishing an efficient multimedia database is the main goal of the music retrieval system. Network music retrieval users are usually unfamiliar with the songs and can only remember a portion of the music track, so it is important to develop a fuzzy algorithm in music search. In this research, the function and frame of various current music retrieval systems are discussed, a com

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10

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 (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 gr

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11

Doraisamy, Shyamala. "Polyphonic music retrieval." ACM SIGIR Forum 39, no. 1 (2005): 58. http://dx.doi.org/10.1145/1067268.1067289.

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12

Lamere, Paul. "Social Tagging and Music Information Retrieval." Journal of New Music Research 37, no. 2 (2008): 101–14. http://dx.doi.org/10.1080/09298210802479284.

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13

Zhang, Jingwen. "Music Data Feature Analysis and Extraction Algorithm Based on Music Melody Contour." Mobile Information Systems 2022 (July 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/8030569.

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Music is a way to reflect people’s real-life emotions, and listening to music has become an inseparable habit of the daily life. Text-based music information retrieval is still the main way for people to find music, but this method has obvious shortcomings and deficiencies, and it is a relatively cumbersome and inefficient method. In order to solve this problem, this paper proposes a feature extraction LAM algorithm based on the contour of music melody. Melody is the most important extraction feature in content-based music retrieval. Users can hum a song according to their own memory and then

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14

Myna, A. N., K. Deepthi, and Samvrudhi V. Shankar. "Hybrid Recommender System for Music Information Retrieval." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4145–49. http://dx.doi.org/10.1166/jctn.2020.9035.

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Music plays an integral role in our lives as the most popular type of recreation. With the advent of new technologies such as Internet and portable media players, large amount of music data is available online which can be distributed and easily made available to people. Enormous amount of music data is released every year by several artists with songs varying in features, genre and so on. Because of this, a need for reliable and easy access of songs based on user preferences is necessary. The recommender system focuses on generating playlists based on the physical, perceptual and acoustical p

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15

Schindler, Alexander, and Andreas Rauber. "Harnessing Music-Related Visual Stereotypes for Music Information Retrieval." ACM Transactions on Intelligent Systems and Technology 8, no. 2 (2017): 1–21. http://dx.doi.org/10.1145/2926719.

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16

Wang, Xiao. "Research of Music Retrieval System Based on Emotional Music Template." Applied Mechanics and Materials 644-650 (September 2014): 3020–23. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3020.

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Traditional music retrieval system based on text information description can't meet people's demand for intelligent retrieval, on which basis content-based music retrieval method came into being. Emotional needs are introduced into retrieval and related researches are done to music retrieval method based on the emotion. This paper first constructs music emotion space to obtain the user's emotions; and then proposes emotional music template library through the study of the definition of emotional music model to meet users emotional needs matching template; Finally, based on this, advances the m

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17

Subramanian, Vignesh. "Music Information Retrieval using Deep Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33850.

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Music Information Retrieval (MIR) is gaining attention due to the surge in digital music and the need for efficient search and recommendation systems. Traditional MIR methods rely on hand-crafted features and rule-based systems, limiting their adaptability. Deep Learning (DL) shows promise in automatically extracting complex patterns from raw data. This paper offers an extensive overview of MIR tasks like classification, genre recognition, similarity search, and recommendation, along with DL models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including LSTM and

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18

Rege, Amit, and Ravi Sindal. "Audio classification for music information retrieval of Hindustani vocal music." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1481–90. https://doi.org/10.11591/ijeecs.v24.i3.pp1481-1490.

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An important task in music information retrieval of Indian art music is the recognition of the larger musicological frameworks, called ragas, on which the performances are based. Ragas are characterized by prominent musical notes, motifs, general sequences of notes used and embellishments improvised by the performers. In this work we propose a convolutional neural network-based model to work on the mel-spectrograms for classication of steady note regions and note transition regions in vocal melodies which can be used for finding prominent musical notes. It is demonstrated that, good classifica

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19

Lee, Jin-Ha. "Natural Language Queries for Music Information Retrieval." Journal of the Korean Society for information Management 25, no. 4 (2008): 149–64. http://dx.doi.org/10.3743/kosim.2008.25.4.149.

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20

Schedl, Markus, Emilia Gómez, and Julián Urbano. "Music Information Retrieval: Recent Developments and Applications." Foundations and Trends® in Information Retrieval 8, no. 2-3 (2014): 127–261. http://dx.doi.org/10.1561/1500000042.

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21

Dave, Kushal, and Vasudeva Varma. "Music Information Retrieval: Recent Developments and Applications." Foundations and Trends® in Information Retrieval 8, no. 4-5 (2014): 263–418. http://dx.doi.org/10.1561/1500000045.

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22

Wibowo, Hardianto, Wildan Suharso, Yufis Azhar, Galih Wasis Wicaksono, Agus Eko Minarno, and Dani Harmanto. "Music Information Retrieval Based on Active Frequency." Makara Journal of Technology 25, no. 2 (2021): 84. http://dx.doi.org/10.7454/mst.v25i2.3977.

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23

Gwardys, Grzegorz, and Daniel Grzywczak. "Deep Image Features in Music Information Retrieval." International Journal of Electronics and Telecommunications 60, no. 4 (2014): 321–26. http://dx.doi.org/10.2478/eletel-2014-0042.

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Abstract Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model trained on CNN were applied to a Music Information Retrieval (MIR), in particular to musical genre recognition. The model was trained on ILSVRC-2012 (more than 1 million natural images) to perform ima

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24

Fingerhut, Michael. "Music Information Retrieval Introduction to the issue." Journal of New Music Research 32, no. 2 (2003): 117–19. http://dx.doi.org/10.1076/jnmr.32.2.117.16741.

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25

Holzapfel, Andre, Bob L. Sturm, and Mark Coeckelbergh. "Ethical Dimensions of Music Information Retrieval Technology." Transactions of the International Society for Music Information Retrieval 1, no. 1 (2018): 44–55. http://dx.doi.org/10.5334/tismir.13.

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26

Flexer, Arthur. "Statistical evaluation of music information retrieval experiments." Journal of New Music Research 35, no. 2 (2006): 113–20. http://dx.doi.org/10.1080/09298210600834946.

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27

Goto, Masataka. "Speech‐recognition interfaces for music information retrieval." Journal of the Acoustical Society of America 118, no. 3 (2005): 2031. http://dx.doi.org/10.1121/1.4785795.

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28

Lippincott, Aura. "Issues in content-based music information retrieval." Journal of Information Science 28, no. 2 (2002): 137–42. http://dx.doi.org/10.1177/016555150202800205.

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29

Wells, Maxwell. "Third International Conference on Music Information Retrieval." Computer Music Journal 27, no. 2 (2003): 108–11. http://dx.doi.org/10.1162/comj.2003.27.2.108.

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30

Cooper, Matthew, Jonathan Foote, Elias Pampalk, and George Tzanetakis. "Visualization in Audio-Based Music Information Retrieval." Computer Music Journal 30, no. 2 (2006): 42–62. http://dx.doi.org/10.1162/comj.2006.30.2.42.

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31

Downie, J. Stephen. "A sample of music information retrieval approaches." Journal of the American Society for Information Science and Technology 55, no. 12 (2004): 1033–36. http://dx.doi.org/10.1002/asi.20054.

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32

Uitdenbogerd, Alexandra L., and Justin Zobel. "An architecture for effective music information retrieval." Journal of the American Society for Information Science and Technology 55, no. 12 (2004): 1053–57. http://dx.doi.org/10.1002/asi.20057.

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33

Rege, Amit, and Ravi Sindal. "Audio classification for music information retrieval of Hindustani vocal music." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1481. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1481-1490.

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Abstract:

An important task in music information retrieval of Indian art music is the recognition of the larger musicological frameworks, called ragas, on which the performances are based. Ragas are characterized by prominent musical notes, motifs, general sequences of notes used and embellishments improvised by the performers. In this work we propose a convolutional neural network-based model to work on the mel-spectrograms for classication of steady note regions and note transition regions in vocal melodies which can be used for finding prominent musical notes. It is demonstrated that, good classifica

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34

Cella, Carmine-Emanuele. "Music Information Retrieval and Contemporary Classical Music: A Successful Failure." Transactions of the International Society for Music Information Retrieval 3, no. 1 (2020): 126–36. http://dx.doi.org/10.5334/tismir.55.

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35

Gedik, Ali C., and Barış Bozkurt. "Pitch-frequency histogram-based music information retrieval for Turkish music." Signal Processing 90, no. 4 (2010): 1049–63. http://dx.doi.org/10.1016/j.sigpro.2009.06.017.

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36

Lee, Jin Ha, Hyerim Cho, and Yea-Seul Kim. "Users' music information needs and behaviors: Design implications for music information retrieval systems." Journal of the Association for Information Science and Technology 67, no. 6 (2015): 1301–30. http://dx.doi.org/10.1002/asi.23471.

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37

Nagavi, Trisiladevi C., and Nagappa U. Bhajantri. "Progressive Filtering Approach for Query by Humming System Through Empirical Mode Decomposition and Multiresolution Histograms." Journal of Intelligent Systems 24, no. 2 (2015): 265–75. http://dx.doi.org/10.1515/jisys-2014-0101.

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AbstractThis research work proposes an implementation of adept content-based music retrieval technique that attempts to address the demands of the rising availability of digital music. The primary objective of this research work is to balance the perilous impact of non-relevant songs through progressive filtering (PF) for query by humming (QBH) music information retrieval system. The PF is a technique of searching in manifolds for problem solving through reduced search space. A new strategy for empirical mode decomposition (EMD) analysis is adopted, and outcomes are propelled as a significant

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38

Ferreri, Laura, Emmanuel Bigand, Patrick Bard, and Aurélia Bugaiska. "The Influence of Music on Prefrontal Cortex during Episodic Encoding and Retrieval of Verbal Information: A Multichannel fNIRS Study." Behavioural Neurology 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/707625.

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Music can be thought of as a complex stimulus able to enrich the encoding of an event thus boosting its subsequent retrieval. However, several findings suggest that music can also interfere with memory performance. A better understanding of the behavioral and neural processes involved can substantially improve knowledge and shed new light on the most efficient music-based interventions. Based on fNIRS studies on music, episodic encoding, and the dorsolateral prefrontal cortex (PFC), this work aims to extend previous findings by monitoring the entire lateral PFC during both encoding and retriev

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39

Rho, Seungmin, Eenjun Hwang, and Jong Hyuk Park. "M-MUSICS: an intelligent mobile music retrieval system." Multimedia Systems 17, no. 4 (2010): 313–26. http://dx.doi.org/10.1007/s00530-010-0212-y.

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40

Lee, Kyoung-Mi. "Mobile Music Album Information Retrieval System using Barcode." Journal of the Korea Contents Association 10, no. 8 (2010): 130–37. http://dx.doi.org/10.5392/jkca.2010.10.8.130.

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41

Levy, M., and M. Sandler. "Music Information Retrieval Using Social Tags and Audio." IEEE Transactions on Multimedia 11, no. 3 (2009): 383–95. http://dx.doi.org/10.1109/tmm.2009.2012913.

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42

Hsu, Huei-Chen. "Evaluation Frameworks for P2P and Music Information Retrieval." Advanced Science Letters 19, no. 9 (2013): 2711–14. http://dx.doi.org/10.1166/asl.2013.4971.

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43

Adams, N. H., M. A. Bartsch, and G. H. Wakefield. "Note segmentation and quantization for music information retrieval." IEEE Transactions on Audio, Speech and Language Processing 14, no. 1 (2006): 131–41. http://dx.doi.org/10.1109/tsa.2005.854088.

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44

Colvin, Jenny. "Naming That Tune: Mobile Music Information Retrieval Systems." Music Reference Services Quarterly 12, no. 1-2 (2009): 29–32. http://dx.doi.org/10.1080/10588160902976407.

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45

Wiering, Frans, Justin de Nooijer, Anja Volk, and Hermi J. M. Tabachneck-Schijf. "Cognition-based Segmentation for Music Information Retrieval Systems." Journal of New Music Research 38, no. 2 (2009): 139–54. http://dx.doi.org/10.1080/09298210903171145.

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46

Herre, Jürgen. "MPEG‐7—Standardized tools for music information retrieval." Journal of the Acoustical Society of America 118, no. 3 (2005): 2030. http://dx.doi.org/10.1121/1.4785791.

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47

Pardo, B. "Finding structure in audio for music information retrieval." IEEE Signal Processing Magazine 23, no. 3 (2006): 126–32. http://dx.doi.org/10.1109/msp.2006.1628889.

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48

Schedl, Markus, Arthur Flexer, and Julián Urbano. "The neglected user in music information retrieval research." Journal of Intelligent Information Systems 41, no. 3 (2013): 523–39. http://dx.doi.org/10.1007/s10844-013-0247-6.

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49

Haus, Goffredo, Maurizio Longari, and Emanuele Pollastri. "A score-driven approach to music information retrieval." Journal of the American Society for Information Science and Technology 55, no. 12 (2004): 1045–52. http://dx.doi.org/10.1002/asi.20056.

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50

Hu, Xiao, and Noriko Kando. "Task complexity and difficulty in music information retrieval." Journal of the Association for Information Science and Technology 68, no. 7 (2017): 1711–23. http://dx.doi.org/10.1002/asi.23803.

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