‘Don’t mess with my algorithm’: Exploring the relationship between listeners and automated curation and recommendation on music streaming services

Given access to huge online collections of music on streaming platforms such as Spotify or Apple Music, users have become increasingly reliant on algorithmic recommender systems and automated curation and discovery features to find and curate music. Based on participant observation and semi-structured interviews with 15 active users of music streaming services, this article critically examines the user experience of music recommendation and streaming, seeking to understand how listeners interact with and experience these systems, and asking how recommendation and curation features define their use in a new and changing landscape of music consumption and discovery. This paper argues that through daily interactions with algorithmic features and curation, listeners build complex socio-technical relationships with these algorithmic systems, involving human-like factors such as trust, betrayal and intimacy. This article is significant as it positions music recommender systems as active agents in shaping music listening habits and the individual tastes of users.

PERLINDUNGAN HUKUM PENCIPTA TERHADAP DOWNLOAD MUSIC STREAMING DARI SEBUAH PLATFORM DIGITAL

Perkembangan teknologi yang pesat tersebut membawa perubahan di segala bidang kehidupan. Hal ini ditandai dengan terciptanya berbagai platform digital yang memudahkan setiap orang yang menampilkan karyanya. Karya-karya yang ditampillkan dalam berbagai platform digital merupakan suatu hasil kemampuan dan kreativitas manusia yang dapat menciptakan Hak Kekayaan Intelektual (selanjutya disebut HKI).Untuk menghormati dan melindungi karya ciptaan tersebut, perlu diadakannya sebuah bentuk perlindungan melalui hak cipta. Penelitian hukum normatif terdiri dari penelitian terhadap asas-asas hukum dengan menekankan pada data sekunder dengan mempelajari dan mengkaji asas-asas atau prinsip-prinsip hukum, baik dalam kaidah hukum positif, kasus-kasus maupun ketentuan-ketentuan perundang-undangan nasional dan internasional yang berkaitan dengan pokok permasalahan yang diteliti. Hasil dari penelitian ini adalah dalam memberikan suatu perlindungan terhadap suatu ciptaan khususnya untuk menghindari adanya penggandaan, pemberian izin dalam penggunaan karya cipta musik dan lagu oleh para pengguna (user) dilakukan dengan perjanjian lisensi disertai dengan adanya pembyaaran royalti kepada pencipta.

Music Genre Classification Using Deep Learning with KNN

Music genre is a conventional category that predicts the genre of music belonging to tradition or set of conventions. A music platform, with total assets of $26 billion, is ruling the music streaming stage today. At present, it has a huge number of tunes and it is information base and claims to have the right music score for everybody. Like, Spotify, Amazon music, Wynk has put a great deal in examination to further develop the manner in which clients find and pay attention to music. AI is at the centre of their examination. From NLP to Collaborative sifting to Deep Learning, All music platforms utilizes them all. Tunes are examined dependent on their advanced marks for certain elements, including rhythm, acoustics, energy, danceability, and so forth, to answer that incomprehensible old first-date inquiry. Organizations these days use music arrangement, either to have the option to put suggestions to their clients (like Spotify, Soundcloud) or just as an item (for instance, Shazam). Deciding music sorts is the initial phase toward that path. AI procedures have ended up being very fruitful in removing patterns and examples from a huge information pool. Similar standards are applied in Music Analysis moreover. Machine learning techniques are achieved in some recent years and rarely in deep learning. Most of the current music genre classification uses Machine learning techniques. In this, we present a music dataset which includes many genres like Rock, Pop, folk, Classical and many genres. A Deep learning approach is used in order to train and classify the system using KNN.

Context-Aware Music Recommender Systems for Groups: A Comparative Study

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

Frontiers: Virus Shook the Streaming Star: Estimating the COVID-19 Impact on Music Consumption

How has the COVID-19 pandemic affected the consumption of audio music streaming?

Scaling Up Broken Systems? Considerations from the Area of Music Streaming

We discuss the effects and characteristics of disruptive business models driven by technology, exemplified by the developments in music distribution and consumption over the last 20 years. Starting from a historical perspective, we offer insights into the current situation in music streaming, where technology has not only changed the way we access music but also has important implications on the broader ecosystem, which includes the consumers, the authors, the record industry, and the platforms themselves. The discussion points to potential benefits, as well as to the risks involved in the currently deployed systems. We conclude that the increased profitability of the disruptive business models in the music domain and beyond is largely generated at the expense of the providers of the goods or services being brokered. Using the platforms as a consumer further subsidizes their value and might lead to mono- and oligopolies. While technology allows companies to effectively scale up business, the resulting systems more often amplify existing injustices than mitigate them.

Factors Influencing Consumer Intention to Subscribe to the Premium Music Streaming Services in China

This study investigates the causes impacting the consumers' intention of the premium music streaming services' subscription in China. An integrated model called the Theory of Streaming Service Acceptance (TSSA) is proposed to explain and predict premium music streaming service subscription behaviors. The TSSA consists of four constructs: attitude, descriptive norm, injunctive norm and perceived behavioral control. The research data was collected in the form of an online survey in China with 120 respondents. Then, interviews were conducted to collect qualitative data from 20 participants. An explanatory sequential mixed method was implemented and the PLS-SEM technique was used to analyze the survey data. The results showed that all constructs in modified research mode, including attitude, injunctive norm and perceived behavioral control except descriptive norm, are indicative predictors for a person’s intention toward premium music streaming services’ subscription. Significant practical inspirations from the perspective of music streaming services providers are also summarized.

Factors Influencing Consumers’ Intention of the Premium Music Streaming Services’ Subscription in China

This study investigates the causes impacting the consumers' intention of the premium music streaming services' subscription in China. An integrated model called the Theory of Streaming Service Acceptance (TSSA) is proposed to explain and predict premium music streaming service subscription behaviors. The TSSA consists of four constructs: attitude, descriptive norm, injunctive norm and perceived behavioral control. The research data was collected in the form of an online survey in China with 120 respondents. Then, interviews were conducted to collect qualitative data from 20 participants. An explanatory sequential mixed method was implemented and the PLS-SEM technique was used to analyze the survey data. The results showed that all constructs in modified research mode, including attitude, injunctive norm and perceived behavioral control except descriptive norm, are indicative predictors for a person’s intention toward premium music streaming services’ subscription. Significant practical inspirations from the perspective of music streaming services providers are also summarized.