Significance and popularity in music production (original) (raw)

Quantifying Music Trends And Facts Using Editorial Metadata From The Discogs Database

2017

While a vast amount of editorial metadata is being actively gathered and used by music collectors and enthusiasts, it is often neglected by music information retrieval and musicology researchers. In this paper we propose to explore Discogs, one of the largest databases of such data available in the public domain. Our main goal is to show how largescale analysis of its editorial metadata can raise questions and serve as a tool for musicological research on a number of example studies. The metadata that we use describes music releases, such as albums or EPs. It includes information about artists, tracks and their durations, genre and style, format (such as vinyl, CD, or digital files), year and country of each release. Using this data we study correlations between different genre and style labels, assess their specificity and analyze typical track durations. We estimate trends in prevalence of different genres, styles, and formats across different time periods. In our analysis of styles we use electronic music as an example. Our contribution also includes the tools we developed for our analysis and the generated datasets that can be re-used by MIR researchers and musicologists. 1 https://discogs.com 2 Discogs mission statement is "to build the biggest and most comprehensive music database and marketplace". 3 https://data.discogs.com/ c Dmitry Bogdanov, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Dmitry Bogdanov, Xavier Serra. "Quantifying music trends and facts using editorial metadata from the Discogs database",

How Last. fm Illustrates the Musical World: User Behavior and Relevant User-Generated Content

… workshop on Visual Interfaces to the …, 2010

Over the last few years, online multimedia exchange platforms have experienced a rapid growth. They allow users to share their own content and access other's in turn and hence form very large public collections of User-Generated Content. While research is mostly looking at photo sharing platforms, such as Flickr, much less is known about online music communities. In this paper we present the results of an observational user study followed by a large-scale online survey, which investigated the behavior and the relevant content generated by the users of Last.fm, one of the most popular music communities. Based on the analysis of the results, we present implications for the usage of User-Generated Content in online music communities. Then we developed a first prototype based on the implications for improving semantic understanding of collaborative tags. We believe our study gives insights for developing information visualization and recommender systems for online music communities.

Proceedings of the International AAAI Conference on Web and Social Media

Recommendations based on the most popular products in a catalogue is a common technique when information about users is scarce or absent. In this paper we explore different ways to measure popularity in the music domain; more specifically, we define four indices based on three social music services and on web clicks. Our study shows, first, that for most of the indices the popularity is a rather stable signal, since it barely changes over time; and second, that the ranking of popular artists is heavily dependent on the actual index used to measure the artist's popularity.

Musically meaningful or just noise? an analysis of on-line artist networks

2009

A sample of the Myspace social network is examined. Using methods from complex network theory, we show empirically that artists tend to form on-line social connections with artists of the same genre. This motivates the use of on-line social networks as data resources for musicology and music information retrieval.

Five approaches to collecting tags for music

2008

We compare five approaches to collecting tags for music: conducting a survey, harvesting social tags, deploying annotation games, mining web documents, and autotagging audio content. The comparison includes a discussion of both scalability (financial cost, human involvement, and computational resources) and quality (the cold start problem & popularity bias, strong vs. weak labeling, vocabulary structure & size, and annotation accuracy). We then describe one state-ofthe-art system for each approach. The performance of each system is evaluated using a tag-based music information retrieval task. Using this task, we are able to quantify the effect of popularity bias on each approach by making use of a subset of more popular (short-head) songs and a set of less popular (long-tail) songs. Lastly, we propose a simple hybrid context-content system that combines our individual approaches and produces superior retrieval results.

American Sociological Review, 2017

In this paper, we propose a new explanation for why certain cultural products outperform their peers to achieve widespread success. We argue that products’ positioning within feature-based association networks—the relational structures formed among sets of similar cultural products—significantly predicts their popular success. Using tools from computer science, we construct a novel data set that allows us to test how the musical features of nearly 27,000 songs from Billboard’s Hot 100 charts structure the consumption of popular music. We find that, in addition to artist familiarity, genre affiliation, and institutional support, a song’s position in its association network influences its position on the charts. Contrary to the claim that all popular music sounds the same, we find that songs sounding too much like their peers—those that are highly typical—are less likely to succeed, while those exhibiting some degree of optimal differentiation are more likely to rise to the top of the charts. These findings offer a new contingent perspective on popular culture by specifying how product association networks organize competition and consumption behavior in cultural markets.

Musical track popularity mining dataset: Extension experimentation

Neurocomputing, 2018

Music Information Research (MIR) requires access to real musical content in order to test the efficiency and effectiveness of its methods as well as to compare developed methodologies on common data. Existing datasets do not address the research direction of musical track popularity that has recently received considerate attention. Moreover, sources of musical popularity do not provide easily manageable data and no standardised dataset exists for musical popularity research. To address these issues the Track Popularity Dataset (TPD) was created in a previous work. TPD provided (a) different sources of popularity definition ranging from 2004 to 2014, (b) mapping between different track/ author/ album identification spaces allowing use of different popularity sources, (c) information on the remaining, non popular, tracks of an album with a popular track, (d) contextual similarity between tracks and (e) ready for MIR use extracted features for both popular and non-popular audio tracks. This paper extends the TPD by (a) adding more readily computed features, (b) proposing feature & similarity definitions on popularity trends, (c) formulating common data mining scenarios on tracks' popularity and (d) presenting respective promising results.

Using online networks to analyse the value of electronic music

In evaluating how creative a program or an artefact is, a key factor to consider is the value inherent in that program or artefact. Our research investigates the process by which cultural products may be accorded a form of specifically cultural value independent of market value, focusing in particular on how that process has been transformed through mediation by online networks. To do this, we are studying a specific artform, i.e. music, and evidence from a specific website, i.e. SoundCloud, making a case study of a specific genre with a special association with that website: electronic music. Quantitative analysis ranges across all genres of music represented on the website, with social network graphs being constructed from relational data and corpus analysis being carried out on textual data. Interviews and observational research are being carried out with electronic music performers in order both to explore what interaction on the site means to them on a qualitative level and to study how the production and circulation of value on the site relates to the production and circulation of value in offline environments. This project will make available a methodology and supporting software for measuring creative value through relevant network analysis.

Significance and popularity in music production (original) (raw)

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