Relevant bibliographies by topics / Music Recommendation Engine

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Music Recommendation Engine'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Music Recommendation Engine"

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Kasinathan, Vinothini, Aida Mustapha, Tan Sau Tong, Mohamad Firdaus Che Abdul Rani, and Nor Azlina Abd Rahman. "Heartbeats: music recommendation system with fuzzy inference engine." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 275. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp275-282.

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<p>In developing a music recommendation system, there are several factors that can contribute to the inefficiency in music selection. One of the problems persists during the music listening is that common music playing application lacks the ability to acquire context of the user. Another problem that common music recommendation system fails to address the is emotional impact of the recommended song. To address this gap, this paper presents a music recommendation system based on fuzzy inference engine that considers user activities and emotion as part of the recommendation parameters. The
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Zhang, Liaoyan. "Optimization of an Intelligent Music-Playing System Based on Network Communication." Complexity 2021 (May 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/9943795.

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Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including r
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Kumar, V. Saravana. "A Novel Classical Music Recommendation System Using User Facial Expression." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5374–79. http://dx.doi.org/10.22214/ijraset.2023.52832.

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Abstract: In today's world, feelings play an important part in many aspects of life. The spectrum of human feelings, whether or not those sensations are verbalised, serves as the foundation for emotions. A diverse range of emotional expressions can be used to convey a person's sense of individuality. It is possible to infer a person's state of mind simply by observing their facial expressions. This project aims to determine people's feelings based on images of them and then match those feelings with suitable musical selections. The majority of today's most popular music discovery tools make us
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Hastuti, Khafiizh, and Khabib Mustafa. "A method for automatic gamelan music composition." International Journal of Advances in Intelligent Informatics 2, no. 1 (2016): 26. http://dx.doi.org/10.26555/ijain.v2i1.57.

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Aim of this study is designing a method for automatic gamelan music composition using rule-base expert system approach. The program is designed for non-expert user in order to help them composing gamelan music or analyzing their composition to achieve explanation and recommendation of ideal composition. There are 2 essential components in this method, which are knowledge and inference. Knowledge is represented into basic knowledge and melodic knowledge. Basic knowledge contains rules that control the structure of gamelan song, and melodic knowledge supports system in composing or analyzing not
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Razlogova, Elena. "Provincializing Spotify: Radio, algorithms and conviviality." Radio Journal:International Studies in Broadcast & Audio Media 18, no. 1 (2020): 29–42. http://dx.doi.org/10.1386/rjao_00014_1.

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Focusing on early experiments with algorithms and music streaming at WFMU, the longest-running US freeform radio station, and the Free Music Archive (FMA), a curated open music website, this article shows how commercial streaming services have been indebted to independent, open music infrastructures but have then erased and denied that history. The article ‘provincializes’ music streaming platforms such as Spotify by focusing not on their commercial aims but instead on the ‘convivial’, collaborative practices and spaces that their software engineers and users inhabited. I analyse an experiment
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Nobuhara, Hajime. "Special Issue on Innovation over Multimedia Processing." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 2 (2012): 211. http://dx.doi.org/10.20965/jaciii.2012.p0211.

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With broadband networks, audio/video coding, and processing techniques and user devices swiftly advancing, multimedia streaming over networks is now a reality. Distributed interactive multimedia applications ? one of the fastest growing market sectors demanding innovations ? are covered in this special issue on multimedia processing (MP). This special issue focuses on ambitious and intriguing papers from experts on a wide variety of multimedia areas. These articles address innovations and offer effective solutions to MP problems. Y. Fushio et al. propose a shadow generation system based on sha
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Pedersen, Rasmus Rex. "Datafication and the push for ubiquitous listening in music streaming." MedieKultur: Journal of media and communication research 36, no. 69 (2020): 071–89. http://dx.doi.org/10.7146/mediekultur.v36i69.121216.

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This article discusses Spotify’s approach to music recommendation as dataficationof listening. It discusses the hybrid types of music recommendation that Spotifypresents to users. The article explores how datafication is connected to Spotify’spush for the personalization and contextualization of music recommendationsbased on a combination of the cultural knowledge found in editorial curation andthe potential for large-scale personalization found in algorithmic curation. Thearticle draws on the concept of ubiquitous music and other understandings ofthe affective and functional aspects of music
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Desai, Mr Chirag, Shubham Bhadra, and Mehul Parekh. "Music Recommendation System using Python." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 699–707. http://dx.doi.org/10.22214/ijraset.2023.54740.

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Abstract: In contrast to the past, the availability of digital music has increased thanks to online music streaming services that can be accessed from mobile phones. It becomes tedious to sort through all of the songs and results in information overload. Many people consider music to be an integral part of their lives and place great value on it. When a person is joyful, depressed, or emotional, he prefers to listen to music to unwind his mind. Users frequently use search engines to find songs of interest to them, but as technology has advanced, other approaches to searching have been adopted.
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narayan, subhashini. "Multilayer Perceptron with Auto encoder enabled Deep Learning model for Recommender Systems." Future Computing and Informatics Journal 5, no. 2 (2020): 96–116. http://dx.doi.org/10.54623/fue.fcij.5.2.3.

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In this modern world of ever-increasing one-click purchases, movie bookings, music, healthcare, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple lay
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Hamilton, Craig. "Popular music, digital technologies and data analysis: New methods and questions." Convergence: The International Journal of Research into New Media Technologies 25, no. 2 (2019): 225–40. http://dx.doi.org/10.1177/1354856519831127.

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This article explores how respondents to The Harkive Project ( www.harkive.org ) are enfolding streaming services and automated recommendation systems into their everyday music reception practices. Harkive is an online project running annually on a single day in July that invites people to provide detail and reflection on their experiences with music. Since the project first ran in 2013, it has gathered over 10,000 individual entries. It is conceived as an ongoing experiment in research methodology that attempts to produce an online social space that encourages reflection from respondents abou
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Dissertations / Theses on the topic "Music Recommendation Engine"

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Bahceci, Oktay. "Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210252.

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Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements an
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Sharma, Govind. "Sentiment-Driven Topic Analysis Of Song Lyrics." Thesis, 2012. https://etd.iisc.ac.in/handle/2005/2472.

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Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA
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Sharma, Govind. "Sentiment-Driven Topic Analysis Of Song Lyrics." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2472.

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Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA)
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Book chapters on the topic "Music Recommendation Engine"

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Mogshetty, Yogesh, Sakshata Vandure, N. Vivekananda, Sammed Vandure, and Bahubali Akiwate. "Music Recommendation Engine Using Emotion Synthesis Through Facial Recognition." In Smart Intelligent Computing and Applications, Volume 2. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9705-0_1.

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Singh, Param, Kamlesh Dutta, Robert Kaye, and Suyash Garg. "Music Listening History Dataset Curation and Distributed Music Recommendation Engines Using Collaborative Filtering." In Proceedings of ICETIT 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_55.

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Lisena, Pasquale, and Raphäel Troncy. "Representing Complex Knowledge for Exploration and Recommendation: The Case of Classical Music Information." In Applications and Practices in Ontology Design, Extraction, and Reasoning. IOS Press, 2020. http://dx.doi.org/10.3233/ssw200038.

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In Digital Humanities, one of the main challenge consists in capturing the structure of complex information in data models and ontologies, in particular when connections between terms are not trivial. This is typically the case for librarian music data. In this chapter, we provide some good practices for representing complex knowledge using the DOREMUS ontology as an exemplary case. We also show various applications of a Knowledge Graph leveraging on the ontology, ranging from an exploratory search engine, a recommender system and a conversational agent enabling to answer classical music questions.
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Norton, Naomi. "Ensemble musicians’ health and wellness." In Together in Music. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198860761.003.0025.

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Relationships between music, health, and wellness are complicated: music can promote health and wellness, but musicians are vulnerable to problems caused or exacerbated by musical activities. With a view towards preventing such problems and promoting musicians’ health and wellness, researchers have mostly focused on individual musicians’ physical and psychological capabilities; however, insights from behavior change science indicate that it is vital to consider the extent to which environmental factors affect musicians’ opportunities to engage in health-promoting behaviors. This chapter focuses on four aspects of ensemble environments that can affect performers’ biological, psychological, and social health and wellness: (1) venue characteristics and configurations; (2) rehearsal organization, goals, and flow; (3) ensemble culture and social norms; and (4) social support and competition. Recommendations for addressing these aspects are presented, based on studies conducted with professional, pre-professional, and amateur ensemble musicians performing instrumental and vocal music from a range of genres.
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Miksza, Peter, Julia T. Shaw, Lauren Kapalka Richerme, Phillip M. Hash, Donald A. Hodges, and Elizabeth Cassidy Parker. "Action Research." In Music Education Research. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/oso/9780197639757.003.0019.

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Abstract This chapter introduces the aims, history, and key features of action research. The chapter also includes discussion of published examples of action research. Action research includes a diverse family of inquiry approaches teachers use to study practice. Action researchers study what is going on now and make plans for or take action within their inquiries. In addition to the reflection that practitioners engage in every day, action researchers intentionally and systematically look at issues with the aim of making a difference in specific ways. Action researchers may utilize either qualitative or quantitative approaches; what distinguishes action research is the spiral of action that includes multiple cycles of look-think-act to systematically study and build sustainable change in the communities under study. Three epistemological beliefs connect various approaches. First, individuals working and living in the setting participate in the research process as participants or co-researchers. Second, one’s professional context is the site for inquiry (e.g., a classroom). Third, through successive cycles of thought, action, and reflection, participants provide recommendations, address issues, and make improvements themselves. Action researchers rarely work alone; many developed their projects in partnership with co-researchers who offered staying power when the realities of overfull teaching schedules made the work challenging.
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Anitha R, Surya Koti Kiran A, Anurag K, and Nikhil Y. "An Efficient Algorithm for Movie Recommendation System." In Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210124.

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Now a day’s recommendation system has changed the fashion of looking the items of our interest. OTT Movie Application Recommendation for mobile users is crucial. It performs a complete aggregation of user preferences, reviews and emotions to help you make suitable movies. It needs every precision and timeliness, however,this can be info filtering approach that’s accustomed predict the preference of that user. Recommender System may be a system that seeks to predict or filter preferences in keeping with the user’s selections. The very common purpose where recommender system is applied are OTT platforms, search engines, articles, music, videos etc During this work we tend to propose a Collaborative approach-based Movie Recommendation system. it is supported collaborative filtering approach that creates use of the knowledge provided by users, analyzes them so recommends the flicks that’s best suited to the user at that point. The suggested motion picture list is sorted in keeping with the ratings given to those movies by previous users. It conjointly helps users to search out of their selections supported the movie expertise of alternative users in economical and effective manner while not wasting a lot of time in useless browsing [1]. Therefore, we tend to offer the item-oriented methodology of the analysis of social network as the steering force of this method to further improve accuracy within the recommendation system. We tend to propose economic healthcare associates during this paper The algorithmic rule of the Film Recommendation supported improved KNN strategy that measures simpler advisory system accuracy. However, to evaluate performance, the k closest victimized neighbors, the maximum inner circles, as well as the basic inner strategies are used [2]. The exception to this is the projected results, which use algorithms to check for (supposedly) involvement.The performance results show that the projected strategies improve additional accuracy of the Movie recommendation system than the other strategies employed in this experiment.
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Joseph, Dawn, and Bradley Merrick. "Sustaining Higher Education Learning in Australia." In Developing Curriculum for Emergency Remote Learning Environments. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6071-9.ch013.

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The COVID-19 pandemic disrupted teaching and learning in higher education institutions globally since March 2020. Tertiary educators restructured modes of delivery to maintain enrolments and engage with students, shifting to remote online learning. This chapter forms part of the study “Reimaging the Future: Music Teaching and Learning, and ICT in Blended Environments in Australia.” It investigates the ways in which tertiary music educators modified teaching practice as they engaged with music technology and information communication technology. Qualitative thematic analyses are employed to code survey data (March-April 2021). Five overarching themes are discussed including constraints and opportunities. This chapter provides additional insights into the growing body of research investigating adaptive approaches to teaching and learning in blended environments. Recommendations identify the need for Australian university educators to prepare graduates with digital and social-emotional competencies in response to the ‘new COVID-19 normal environment'.
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Elangovan, Ramanujam. "The Dark Web." In Encyclopedia of Criminal Activities and the Deep Web. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9715-5.ch008.

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The deep web (also called deepnet, the invisible web, dark web, or the hidden web) refers to world wide web content that is not part of the surface web, which is indexed by standard search engines. The more familiar “surface” web contains only a small fraction of the information available on the internet. The deep web contains much of the valuable data on the web, but is largely invisible to standard web crawling techniques. Besides it being the huge source of information, it also provides the rostrum for cybercrime like by providing download links for movies, music, games, etc. without having their copyrights. This article aims to provide context and policy recommendations pertaining to the dark web. The dark web's complete history, from its creation to the latest incidents and the way to access and their sub forums are briefly discussed with respective to the user perspective.
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Conference papers on the topic "Music Recommendation Engine"

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Sha, Jasper, Nathaniel Basara, Joseph Freedman, and Hailu Xu. "FLOR: A Federated Learning-based Music Recommendation Engine." In 2022 International Conference on Computer Communications and Networks (ICCCN). IEEE, 2022. http://dx.doi.org/10.1109/icccn54977.2022.9868921.

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