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Journal articles on the topic 'MUSIC INFORMATION RETRIEVAL SYSTEMS'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 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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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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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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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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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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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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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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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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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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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

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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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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14

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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15

Merkley, Cari. "Music Information Seeking Behaviour Poses Unique Challenges for the Design of Information Retrieval Systems." Evidence Based Library and Information Practice 5, no. 4 (2010): 90. http://dx.doi.org/10.18438/b8t621.

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Objective – To better understand music information seeking behaviour in a real life situation and to create a taxonomy relating to this behaviour to facilitate better comparison of music information retrieval studies in the future.  Design – Content analysis of natural language queries.  Setting – Google Answers, a fee based online service.  Subjects – 1,705 queries and their related answers and comments posted in the music category of the Google Answers website before April 27, 2005.  Methods – A total of 2,208 queries were retrieved from the mu

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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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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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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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19

Chen, Yan. "Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques." Applied and Computational Engineering 87, no. 1 (2024): 197–202. http://dx.doi.org/10.54254/2755-2721/87/20241564.

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Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into h

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20

Orio, Nicola. "Music Retrieval: A Tutorial and Review." Foundations and Trends® in Information Retrieval 1, no. 1 (2006): 1–96. http://dx.doi.org/10.1561/1500000002.

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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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22

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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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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23

Downie, J. Stephen. "The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future." Computer Music Journal 28, no. 2 (2004): 12–23. http://dx.doi.org/10.1162/014892604323112211.

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24

Zalkow, Frank, Julian Brandner, and Meinard Müller. "Efficient Retrieval of Music Recordings Using Graph-Based Index Structures." Signals 2, no. 2 (2021): 336–52. http://dx.doi.org/10.3390/signals2020021.

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Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. A

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Patil, Swati A., and Thirupathi Rao Komati. "Designing of a Novel Neural Network Model for Classification of Music Genre." Ingénierie des systèmes d information 27, no. 2 (2022): 327–33. http://dx.doi.org/10.18280/isi.270217.

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Music genre classification is an important task that entails classifying music genres based on aural data. Music genre classification is widely used in the field of music information retrieval. Data preparation, feature extraction, and classification are the three primary processes in the proposed system. New neural network is used to classify music genres. To categorize songs into respective music genres, the proposed system leverages feature values from spectrograms created from slices of songs as input to a proposed system architecture. Extensive tests on the GTZAN dataset demonstrate the e

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Vasu, Karthik, and Savita Choudhary. "Music Information Retrieval Using Similarity Based Relevance Ranking Techniques." Scalable Computing: Practice and Experience 23, no. 3 (2022): 103–14. http://dx.doi.org/10.12694/scpe.v23i3.2005.

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The purpose of this proposed study activity is to construct a system for the job of automatically assessing the relevance of music datasets, which will be used in future work. Determine item similarity is an important job in a recommender system since it determines if two items are similar. Participants' systems must provide a list of suggested music that may be added to a given playlist based on a set of playlist characteristics, {which will work along with the algorithms designed to provide other similar songs. Specifically, in this study, the challenges of detecting music similarity only on

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27

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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28

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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29

Tzanetakis, George, and Perry Cook. "Music analysis and retrieval systems for audio signals." Journal of the American Society for Information Science and Technology 55, no. 12 (2004): 1077–83. http://dx.doi.org/10.1002/asi.20060.

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30

Lai, Katie. "An Examination of Faceted Searching in Discovery Systems and the Impact on Information Discovery." CAML Review / Revue de l'ACBM 52, no. 1 (2024): 33–59. http://dx.doi.org/10.25071/1708-6701.40479.

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This study compares the performances of the resource type facets and format facets in Primo and WorldCat Discovery respectively. Through looking at librarians’ perceived understanding of selected facets, the information retrieval mechanisms employed, and the search results yielded, the author reveals gaps between users’ perception and the information actually retrieved. The goals are to see how successful Primo and WorldCat Discovery are in making themselves a one-stop shop for music information, and to determine whether the resource type or format facets in these tools facilitate information

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Mazur, Zygmunt, and Konrad Wiklak. "MSALSA - a method of positioning search results in music information retrieval systems." International Journal of Intelligent Information and Database Systems 9, no. 3/4 (2016): 232. http://dx.doi.org/10.1504/ijiids.2016.081598.

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Wiklak, Konrad, and Zygmunt Mazur. "MSALSA - a method of positioning search results in music information retrieval systems." International Journal of Intelligent Information and Database Systems 9, no. 3/4 (2016): 232. http://dx.doi.org/10.1504/ijiids.2016.10002510.

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33

Budyputra, M. Aqila, Achmad Reyfanza, Alexander Agung Santoso Gunawan, and Muhammad Edo Syahputra. "Systematic Literature Review of The Use of Music Information Retrieval in Music Genre Classification." International Journal of Computer Science and Humanitarian AI 2, no. 1 (2025): xx. https://doi.org/10.21512/ijcshai.v2i1.13019.

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Emphasizing deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this article explores the application of Music Information Retrieval (MIR) techniques in music genre categorization. These algorithms outperform traditional methods in capturing complex audio patterns, showcasing their potential in advancing music classification tasks. Accurate genre classification critically depends on key features such as spectral, temporal, and timbral characteristics, which play a pivotal role in distinguishing musical styles. However, the performance of thes

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Uitdenbogerd, Alexandra L., Abhijit Chattaraj, and Justin Zobel. "Methodologies for Evaluation of Note-Based Music-Retrieval Systems." INFORMS Journal on Computing 18, no. 3 (2006): 339–47. http://dx.doi.org/10.1287/ijoc.1050.0139.

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Diputra Wiraguna, I. Gusti Agung Ngurah, and Luh Arida Ayu Rahning Putri. "Klasifikasi Genre Musik Menggunakan Support Vector Machine Berdasarkan Spectral Features." Jurnal Nasional Teknologi Informasi dan Aplikasnya 1, no. 3 (2023): 933. https://doi.org/10.24843/jnatia.2023.v01.i03.p20.

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This research focuses on music genre classification based on spectral features and Support Vector Machine (SVM). Features such as Spectral Centroid, Spectral Rolloff, Spectral Flux, and Spectral Bandwidth are extracted from MP3 music audio. The dataset comprising 4 music genres is utilized for training and testing the system. The extracted spectral features are fed into the SVM classifier to predict the genre of test samples. Python and machine learning are both used in developing the system while the experimental results demonstrate the effectiveness of SVM in accurately classifying music gen

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Kostek, Bozena. "Application of soft computing to automatic music information retrieval." Journal of the American Society for Information Science and Technology 55, no. 12 (2004): 1108–16. http://dx.doi.org/10.1002/asi.20064.

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Rao, Preeti, Hema A. Murthy, and Ajay Srinivasamurthy. "Report on the 23rd International Society for Music Information Retrieval Conference (ISMIR 2022)." ACM SIGIR Forum 57, no. 1 (2023): 1–15. http://dx.doi.org/10.1145/3636341.3636350.

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The annual International Society for Music Information Retrieval (ISMIR) conference is the world's leading research forum on processing, searching, organizing and accessing music-related data. The 23rd International Society for Music Information Retrieval Conference (ISMIR 2022) was held from 04--08 December, 2022 at the National Science Seminar Complex, Indian Institute of Science, Bengaluru, India. ISMIR 2022 is the first ever ISMIR conference to take place in India. Due to the changing global landscape as a result of the COVID-19 pandemic, the 23rd ISMIR conference became the first hybrid I

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Rousi, Antti Mikael, Reijo Savolainen, Maaria Harviainen, and Pertti Vakkari. "Situational relevance of music information modes." Journal of Documentation 74, no. 5 (2018): 1008–24. http://dx.doi.org/10.1108/jd-10-2017-0149.

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Purpose The purpose of this paper is to elaborate the picture of situational relevance of music information from a performing musician’s point of view by delving into its diverse layers within the context of Doctor of Music students’ information seeking. Design/methodology/approach Music-related information is approached through six modes that categorize music information sources based on their levels of abstraction. Situational relevance of the modes of music information is examined in relation to the situational requirements of accomplishing a dissertation on music task consisting of both a

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Hertzum, Morten, and Pia Borlund. "Music questions in social Q&A: an analysis of Yahoo! Answers." Journal of Documentation 73, no. 5 (2017): 992–1009. http://dx.doi.org/10.1108/jd-02-2017-0024.

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Purpose Social question and answer (social Q&A) sites have become a popular tool for obtaining music information. The purpose of this paper is to investigate what users ask about, what experience the questions convey, and how users specify their questions. Design/methodology/approach A total of 3,897 music questions from the social Q&A site Yahoo! Answers were categorized according to their question type, user experience, and question specification. Findings The music questions were diverse with (dis)approval (42 percent), factual (21 percent), and advice (15 percent) questions as the

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Minor, Kelvin A., and Iman H. Kartowisastro. "Automatic Music Transcription Using Fourier Transform for Monophonic and Polyphonic Audio File." Ingénierie des systèmes d information 27, no. 4 (2022): 629–35. http://dx.doi.org/10.18280/isi.270413.

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Musical sheet is an important tool for musicians that enables musicians to communicate with each other and help musicians to learn a composition of a song. Sometimes, musicians face an obstacle when they cannot find the musical sheet to learn a new song or it may require payment to get the sheet. The solution for this problem is to learn the song by figuring out the composition of a song using music transcription. Music Transcription is the process of music information retrieval to produce musical notation. Music Transcription using a computational method often called Automatic Music Transcrip

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41

Byrd, Donald, and Tim Crawford. "Problems of music information retrieval in the real world." Information Processing & Management 38, no. 2 (2002): 249–72. http://dx.doi.org/10.1016/s0306-4573(01)00033-4.

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42

Hu, Xiao. "Evaluating mobile music services in China: An exploration in user experience." Journal of Information Science 45, no. 1 (2018): 16–28. http://dx.doi.org/10.1177/0165551518762070.

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Most digital music repositories and services have mobile applications (apps) that facilitate convenient access for users via smartphones. Although China has one of the largest music listener populations in the world, there is little research evaluating Chinese online or mobile music services. To bridge this gap, this study evaluated mobile apps of three of the most popular Chinese music services from the user’s perspective, using usability testing and semi-structured interviews with a sample of active users in China. Nielsen’s 10 user experience heuristics and four criteria in recommender eval

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Tran, Thi Thanh. "Analysis of Building the Music Feature Extraction Systems: A Review." Engineering and Technology Journal 9, no. 05 (2024): 4055–60. https://doi.org/10.5281/zenodo.11242886.

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Music genre classification is a basic method for sound processing in the field of music retrieval. The application of machine learning has become increasingly popular in automatically classifying music genres. Therefore, in recent years, many methods have been studied and developed to solve this problem. In this article, an overview on the process and some music feature extraction methods is presented. Here, the feature extraction method using Mel Frequency Cepstral Coefficients (MFCC) is discussed in detail. Some typical results in using Mel Frequency Cepstral Coefficients for improving accur

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Weigl, David M., and Catherine Guastavino. "Applying the stratified model of relevance interactions to music information retrieval." Proceedings of the American Society for Information Science and Technology 50, no. 1 (2013): 1–4. http://dx.doi.org/10.1002/meet.14505001135.

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Cui, Xiaohui, Xiaolong Qu, Dongmei Li, Yu Yang, Yuxun Li, and Xiaoping Zhang. "MKGCN: Multi-Modal Knowledge Graph Convolutional Network for Music Recommender Systems." Electronics 12, no. 12 (2023): 2688. http://dx.doi.org/10.3390/electronics12122688.

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With the emergence of online music platforms, music recommender systems are becoming increasingly crucial in music information retrieval. Knowledge graphs (KGs) are a rich source of semantic information for entities and relations, allowing for improved modeling and analysis of entity relations to enhance recommendations. Existing research has primarily focused on the modeling and analysis of structural triples, while largely ignoring the representation and information processing capabilities of multi-modal data such as music videos and lyrics, which has hindered the improvement and user experi

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Cornelis, Olmo, Micheline Lesaffre, Dirk Moelants, and Marc Leman. "Access to ethnic music: Advances and perspectives in content-based music information retrieval." Signal Processing 90, no. 4 (2010): 1008–31. http://dx.doi.org/10.1016/j.sigpro.2009.06.020.

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Tavares, Tiago Fernandes, and Flávio Luiz Schiavoni. "Contributions on Computer Music from the SBCM 2019." Revista de Informática Teórica e Aplicada 27, no. 4 (2021): 139–42. http://dx.doi.org/10.22456/2175-2745.116606.

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The Brazilian Symposia on Computer Music are events that foster a rich environment for exciting interdisciplinary discussion. In its 17th edition, in 2019, the event was held in São João Del Rei, MG. This special issue presents 5 selected papers from the conference's technical program covering different research fields like sound synthesis, music information retrieval, sound systems, and digital musical instruments.

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Chaudhary, Dr Seema R. "A Review on Feature Extraction Using CNN in Musical Instrument Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42982.

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This literature review explores the use of convolutional neural networks (CNNs) for the classification of musical instruments within music information retrieval. It highlights the limitations of traditional manual feature extraction methods and emphasizes the advantages of deep learning in automating feature extraction from raw audio data. By focusing on time-frequency representations, CNNs have demonstrated significant effectiveness in identifying relevant features crucial for tasks such as music genre identification, transcription, and music recommendation systems. Key Words: feature extract

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Lemström, Kjell, Niko Mikkilä, and Veli Mäkinen. "Filtering methods for content-based retrieval on indexed symbolic music databases." Information Retrieval 13, no. 1 (2009): 1–21. http://dx.doi.org/10.1007/s10791-009-9097-9.

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Jiau, Hewijin Christine, and Chuan-Wang Chang. "A Dual Ternary Indexing Approach for Music Retrieval System." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 3 (2008): 227–33. http://dx.doi.org/10.20965/jaciii.2008.p0227.

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Memory usage for storing indexes and query response times for retrieval processing are two critical issues in music information retrieval (MIR) systems. In this paper, we propose an effective and efficient numeric indexing structure to overcome the difficulties of variable length queries and enhance the efficiency of music retrieval. The proposed structure differs greatly from pre-existing research in textual indexing techniques such asn-gram and suffix tree because it does not need to generate redundant and useless indexes. The index construction process has no complicated split and joint ope

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