4. Music 2.0

2021 ◽  
pp. 86-111
Author(s):  
Nicholas Cook
Keyword(s):  
Digital Technology ◽  
Historical Development ◽  
Recommendation Systems ◽  
Music Recommendation ◽  
Musical Culture ◽  
Music Business ◽  
Internet Communities ◽  
Digital Sound

This chapter explains the role technology plays in all music: digital technology is simply its most recent manifestation. After reviewing the basics of digital sound, the chapter outlines the transformational effect of digital technology on musical culture. Particular attention is given to its impact on personal musical consumption, including the development of streaming libraries and music recommendation systems, and on the development of internet communities based on digital participation; a result has been the development of a distinctive ‘digital style’, illustrated for example by internet memes. Finally the chapter outlines the historical development of the music business, and the radical ways in which it has been—and is being—transformed by technology.

Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

2011 ◽  
pp. 203-221 ◽  
Author(s):  
Zehra Cataltepe ◽  
Berna Altinel
Keyword(s):  
Demographic Data ◽  
Recommendation Systems ◽  
Personal Data ◽  
Music Recommendation ◽  
Music Preferences ◽  
User Models ◽  
Online Music ◽  
Important Field ◽  
Case Based

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.

Knowledge Management Techniques in Emotion-Based Music Recommendation Systems

2021 ◽  
pp. 415-423
Author(s):  
Catherine Marinagi ◽  
Paris Ntsounos ◽  
John Darryl Pelingo ◽  
Christos Skourlas ◽  
Anastasios Tsolakidis
Keyword(s):  
Knowledge Management ◽  
Recommendation Systems ◽  
Music Recommendation ◽  
Management Techniques

scholarly journals Recommendation Systems as Technologies of the Self: Algorithmic Control and the Formation of Music Taste

2017 ◽  
Vol 35 (2) ◽  
pp. 3-24 ◽  
Author(s):  
Nedim Karakayali ◽  
Burc Kostem ◽  
Idil Galip
Keyword(s):  
Soft Power ◽  
Recommendation Systems ◽  
The Self ◽  
Music Recommendation ◽  
Web Technologies ◽  
Technologies Of The Self ◽  
Cultural Medium ◽  
Care Practices ◽  
Creative Self ◽  
To Receive

The article brings to light the use of recommender systems as technologies of the self, complementing the observations in current literature regarding their employment as technologies of ‘soft’ power. User practices on the music recommendation website last.fm reveal that many users do not only utilize the website to receive guidance about music products but also to examine and transform an aspect of their self, i.e. their ‘music taste’. The capacity of assisting users in self-cultivation practices, however, is not unique to last.fm but stems from certain properties shared by all recommendation systems. Furthermore, unlike other oft-studied digital/web technologies of the self which facilitate ‘self-publishing’ vis-à-vis virtual companions in social media, recommender algorithms themselves can act as ‘intimate experts’, accompanying users in their self-care practices. Thus, recommendation systems can facilitate both algorithmic control and creative self-transformation, which calls for a theorization of this new cultural medium as a space of tension.

scholarly journals DAGESTAN PROFESSIONAL MUSICAL ART IN HISTORICAL DEVELOPMENT: THE SECOND HALF OF THE XX - BEGINNING OF THE XXI CENTURY

2021 ◽  
Vol 17 (2) ◽  
pp. 363-386
Author(s):  
Elmira B. Abdullaeva
Keyword(s):  
Historical Development ◽  
Classical Music ◽  
Species Differentiation ◽  
Research Experience ◽  
Musical Culture ◽  
Holistic View ◽  
Modern Culture ◽  
The Look ◽  
Historical Periods ◽  
Actual Part

The article describes the patterns and features of the development of the musical art of Dagestan at different stages of its evolution over more than half a century of history. We have analyzed the components of the musical and professional tradition, giving a holistic view of it, versatile reflecting both the originality and originality, and historical variability. These include: genre-species differentiation and systemic connections, stylistics and means of musical expression.The multifaceted study and the possibility of interpreting the data obtained allows one to create an idea of ​​the ways of the formation and development of the musical art of Dagestan during the period under consideration. The initial premises of the study can be summarized as follows.The structure of musical art is formed on the basis of the interrelationships of composer's creativity, performing practice and various cultural interchanges that undergo stylistic and genre-specific changes.The second premise was the look at the musical art of Dagestan as an actual part of modern culture. Therefore, the main source has become various forms of broadcasting musical culture (listening practice and analytical observations at concerts of classical music).Reliance on contemporary musical material and direct observation of the musical process in the field of classical, pop and other spheres of culture presupposes the study of the phenomenon in a synchronic aspect. The presence of publications by different authors and our own research experience make it possible to a certain extent to make diachronic comparisons.The important regularities in the development of the Dagestan academic musical art identified by us can form the basis for further research of genre phenomena in different historical periods.

scholarly journals Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System

2020 ◽  
Vol 9 (1) ◽  
pp. 1548-1553
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
Correlation Matrix ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Vital Role ◽  
Music Recommendation ◽  
Filtering Method ◽  
Music Recommendation System

Music recommendation systems are playing a vital role in suggesting music to the users from huge volumes of digital libraries available. Collaborative filtering (CF) is a one of the well known method used in recommendation systems. CF is either user centric or item centric. The former is known as user-based CF and later is known as item-based CF. This paper proposes an enhancement to item-based collaborative filtering method by considering correlation among items. Lift and Pearson Correlation coefficient are used to find the correlation among items. Song correlation matrix is constructed by using correlation measures. Proposed method is evaluated on the benchmark dataset and results obtained are compared with basic item-based CF

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

Culture Industries in the Digital Age: Music Business crisis in the XXI Century

2014 ◽  
pp. 4-11
Author(s):  
Julia V. Strakovich
Keyword(s):  
Digital Age ◽  
Social Practices ◽  
Contemporary Culture ◽  
Musical Culture ◽  
Music Business ◽  
Culture Industries ◽  
Technological Revolution ◽  
Great Transformation ◽  
Business Crisis

Deals with contemporary culture which undergoes the great transformation caused by the technological revolution unfolding on the verge of the XXI century. Yesterday’s warrants of social and cultural stability are replaced by today’s new culture norms and social practices. One of this transformation results is crisis of culture industries. In attempt to discuss key reasons of this crisis, the focus of this article was placed on musical culture

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