[Purpose/significance] Aiming at the problem of low matching degree of information organization and retrieval demand caused by the rapid growth of electronic dance music digital resources, this paper tries to construct a demand model of electronic dance music information retrieval to guide the organization of electronic dance music information under the new network environment. [Method/process] Through the retrieval experiment of mainstream music platforms in China, the paper drew the outline of the interviews of the electronic dance music retrieval needs, and then used the grounded theory to explore the music information retrieval needs of the electronic dance music listeners and constructed the information retrieval demand model. [Result/conclusion] The study found that the audience demand of electronic dance music has "growth", which connects the aesthetic subject (audience) and aesthetic object (music) of the audience information retrieval needs, and the internal promotion of "growth" is the Matthew effect of information needs. The research indicated that the information organization of electronic dance music needs to balance the core dimensions such as subjective feelings, external description, content elements and production process. The paper can provide a reference for the organization of electronic dance music information under the new network environment.
Zhang Yuanming
,
Zhang Yunzhong
,
Liu Di
. Growth Model of Demand for Information Retrieval Based on Grounded Theory: A Case Study of Electronic Dance Music[J]. Library and Information Service, 2018
, 62(11)
: 35
-42
.
DOI: 10.13266/j.issn.0252-3116.2018.11.004
[1] 张小夫.电子音乐的概念界定[J].中央音乐学院学报,2002(4):30-32.
[2] 王敏虹. 广东民间音乐信息需求调查[J]. 图书馆论坛, 2017, 37(3):128-132.
[3] LEE J H. Analysis of user needs and information features in natural language queries seeking music information[J]. Journal of the association for information science and technology, 2010, 61(5):1025-1045.
[4] 杜亚雄."五大类"还是"四大类"?——对中国传统音乐教学改革的建议[J].中国音乐,2013(2):16-18.
[5] LACOMBE H. On differentiation and classification-reflections on the idea of 19th-century French lyrical genres-creating new concepts for "institutional places" in music[J]. Revue de musicologie,1998,84(2):247-262.
[6] 郭旗.关于音乐图书馆统一使用分类法的意见[J].中国音乐学,1993(2):91-92.
[7] 王天兵,宋显彪.《中国图书馆分类法》(第5版)"J6音乐"类目修订评述[J].图书馆建设,2013(6):30-33.
[8] PAMPALK E, FLEXER A, WIDMER G. Hierarchical organization and description of music collections at the artist level[C]//International conference on theory and practice of digital libraries. Berlin:Springer, 2005:37-48.
[9] 吴丹,史劲松.古典音乐录音资源本体构建研究[J].图书情报工作,2014,58(22):117-124.
[10] MADALLI D P, BALAJI B P, SARANGI A K. Faceted ontological representation for a music domain[J]. Knowledge Organization, 2015, 42(1):8-24.
[11] 董高洁.基于folksonomy标签的音乐本体构建研究[D].郑州:郑州大学,2015.
[12] 楼巧巧.基于增强学习的个性化音乐情感分类系统研究[D].杭州:浙江大学,2015.
[13] 庄严.基于声学特征和音乐特征的音乐流派分类研究[D].镇江:江南大学,2014.
[14] BARUAH T, TIWARI S. Intelligent classification of electronic music[C]//Signal processing and information technology. New York:IEEE, 2014:31-35.
[15] VAN V A. A community ecology of genres:explaining the emergence of new genres in the UK field of electronic/dance music, 1985-1999[J]. Poetics, 2015, 52(9):104-123.
[16] 韩圣龙.音乐元数据研究现状[J].大学图书馆学报,2007(2):67-72.
[17] FRIEDICH M, KAYE R. Musicbrainz metadata initiative 2.1[DB/OL].[2016-11-04]. http://musiebrainz.org/MM.
[18] 李静.基于用户情感标签的音乐检索算法[D].大连:大连理工大学,2010.
[19] CASEY M A, VELTKAMP R, GOTO M, et al. Content-based music information retrieval:current directions and future challenges[J]. Proceedings of the IEEE, 2008, 96(4):668-696.
[20] 张良.基于压缩感知的音乐识别研究[D].天津:天津大学,2010.
[21] 费小冬.扎根理论研究方法论:要素、研究程序和评判标准[J].公共行政评论,2008(3):23-43.
[22] 李重光. 音乐理论基础[M].北京:人民音乐出版社, 2000.
[23] HRACE B J. A creative industry in transition:the rise of digitally driven independent music production[J]. Growth and Change, 2012, 43(3):442-461.
[24] HOWARD D M, ANGUS J. Acoustics and psychoacoustics[M]. Abingdon:Taylor & Francis, 2017.
[25] 薛锋,杨宗英,郑巧英,等.基于内容的音乐检索[J].大学图书馆学报,1999,(04):28-30. 作者贡献说明:张原铭:拟定研究主题,设计研究方法及方案,采集数据并编码,分析数据,构建模型,撰写研究论文; 张云中:指导研究选题,在数据的采集、编码、分析及模型构建过程中提出建议,指导论文的修改; 柳迪:参与数据采集及编码工作,为数据分析和模型构建提供参考意见。
