Challenges in cross-cultural/multilingual music information seeking

Understanding and meeting the needs of a broad range of music users across different cultures and languages are central in designing a global music digital library. This exploratory study examines cross-cultural/multilingual music information seeking behaviors and reveals some important characteristics of these behaviors by analyzing 107 authentic music information queries from a Korean knowledge search portal Naver (knowledge) iN and 150 queries from Google Answers website. We conclude that new sets of access points must be developed to accommodate music queries that cross cultural or language boundaries

A taxonomy of digital music services

This paper investigates the current status of what we call Digital Music Services (DMS). Our research objective is gaining a better understanding of the properties of current DMS and investigating the relationship between service functionality and business model. In a longitudinal research project multiple researchers have contributed to making a taxonomy of current DMS. By recurrent induction and systematic comparison we derive four archetypes of DMS; Extended Radio Broadcasting (EBR), Personalized Internet Radio (PIR), Music Exchange Community (MEC) and Online Music retailers (OMR). A metaanalysis shows that the business model, service functionality and recommendation mechanism are the identifying characteristics of current DMS. We observe a tendency of DMS to start “drifting” between the archetypes and adopting properties from other archetypes. This is not without risk, as users have a rather fixed perspective of the relationship between the offered functionality and business model of the respective archetype

K-Pop Genres: A Cross-Cultural Exploration

The Proceedings can be viewed at: http://www.ppgia.pucpr.br/ismir2013/wp-content/uploads/2013/10/Proceedings-ISMIR2013-Final.pdfPoster Session 3Current music genre research tends to focus heavily on classical and popular music from Western cultures. Few studies discuss the particular challenges and issues related to non-Western music. The objective of this study is to improve our understanding of how genres are used and perceived in different cultures. In particular, this study attempts to fill gaps in our understanding by examining K-pop music genres used in Korea and comparing them with genres used in North America. We provide background information on K-pop genres by analyzing 602 genre-related labels collected from eight major music distribution websites in Korea. In addition, we report upon a user study in which American and Korean users annotated genre information for 1894 K-pop songs in order to understand how their perceptions might differ or agree. The results show higher consistency among Korean users than American users demonstrated by the difference in Fleiss’ Kappa values and proportion of agreed genre labels. Asymmetric disagreements between Americans and Koreans on specific genres reveal some interesting differences in the perception of genres. Our findings provide some insights into challenges developers may face in creating global music services.published_or_final_versio

The adoption of personalized music services – Combining qualitative and quantitative research –

In the last decade the music industry has been developing different Internet based music services. Lately personalization via recommendation is gaining popularity. In this paper we investigate the adoption of personalized music services by a combined quantitative and qualitative research approach. We first deploy an adoption study by the use of an adapted TAM survey. Our quantitative findings confirm perceived enjoyment as influential factor for intention to use, higher than perceived usefulness. Instead of broadening the quantitative study to a wider group of users we investigate deeper with qualitative interviews based on diffusion of innovation and different adoption models. Firstly three hypotheses are formulated on basis of the survey. Secondly our qualitative results give a richer explanation and show our group of respondents value the quality of the music recommendation mechanism over extra other functionalities like social networking, blogging and scrobbling. The latter result is important for music service suppliers in their highly competitive market

Content Management for the Live Music Industry in Virtual Worlds: Challenges and Opportunities

International audienceThe real-world music industry is undergoing a transition away from the retailing and distribution of fixed objects (records, files) to the consumption of live,interactive events (concerts, happenings). This development is paralleled with the recent flourishing of live music in virtual worlds, which in many ways could become the epitome of its real-world counterpart: for the artists, virtual concerts are cheap and easy to organize, and can therefore be a viable alternative to performing in the real world; for the music promoter and marketer, virtual concert attendance can be traced and analyzed more easily than in the real world; for the virtual concertgoer, attending concerts that are happening a (virtual) world away is possible with a single click. Taking insights from both a survey among the Second-Life music practitioners and from our own prototype of a live music recommendation system built on top of Second-Life, this article shows that the technical infrastructure of current virtual worlds is not well-suited to the development of the content management tools needed to support this opportunity. We propose several new ways to address these problems, and advocate for their recognition both by the artistic and the technical community

A scalable Peer-to-Peer System for Music Content and Information Retrieval

Currently a large percentage of internet traffice consists of music files, typically stored in MP3 compressed audio format, shared and exchanged over Peer-to-Peer (P2P) networks. Searching for music is performed by specifying keywords and naive string matching techniques. In the past years the emerging research area of Music Information Retrieval (MIR) has produced a variety of new ways of looking at the problem of music search. Such MIR techniques can significantly enhance the ways users search for music over P2P networks. In order for that to happen there are two main challenges that need to be addressed: 1) scalability to large collections and number of peers 2) richer set of search semantics that can support MIR especially when retrieval is content-based. In this paper, we describe a scalable P2P system that uses Rendezvouz Points (RPs) for music metadata registration and query resolution, that supports atribute-value search semantics as well as content-based retrieval. The performance of the system has been evaluated in large scale usage scenarios using "real" automatically calculated musical content descriptors

A Novel Framework for Efficient Automated Singer Identification in Large Music Databases

10.1145/1508850.1508856ACM Transactions on Information Systems273ATIS

Verifying tag annotation and performing genre classification in music data via association analysis

Music Information Retrieval aims to automate the access to large-volume music data, including browsing, retrieval, storage, etc. The work presented in this thesis tackles two non-trivial problems in the field. First problem deals with music tags, which provide descriptive and rich information about a music piece, including its genre, artist, emotion, instrument, etc. At present, tag annotation is largely a manual process, which often results in tags that are subjective, ambiguous, and error-prone. We propose a novel approach to verify the quality of tag annotation in a music dataset through association analysis. Second, we employ association analysis to predict music genres based on features extracted directly from music. We build an association-based classifier, which finds inherent associations between music features and genres. We demonstrate the effectiveness of our approaches through a series of simulations and experiments using various benchmark music datasets