Modeling of Music Connotative Space for Recommender System

doi:10.7652/xjtuxb201404006

您当前的位置：

首页 >

文章列表页 >

Modeling of Music Connotative Space for Recommender System

更新时间：2025-04-29

- Modeling of Music Connotative Space for Recommender System
- Vol. 48, Issue 4, Pages: 31-34(2014)
- 作者机构：
  
  新疆大学网络与信息技术中心,乌鲁木齐,830046
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201404006
  CLC： TP391
- Online First：10 April 2014，
  
  Published：2014
- 稿件说明：
移动端阅览
Modeling of Music Connotative Space for Recommender System[J]. 2014, 48(4): 31-34.
DOI：

Modeling of Music Connotative Space for Recommender System[J]. 2014, 48(4): 31-34. DOI： 10.7652/xjtuxb201404006.

摘要

针对现有音乐推荐系统中音乐情感分析方法很难满足用户情感需求的问题

提出音乐内涵空间方法分析音乐情感。该方法选取音乐力度、速度、音强等表现要素

基于两极尺度的语义

构建音乐内涵空间。通过音乐内涵空间表示音乐情感

减少了音频特征客观水平与主观情感范围之间的差距

解决了情感标签标注音乐中由于用户情感经历不同造成的对音乐情感标注的主观差异性问题。在10 672条音乐评价数据集上

采用Kendall's tau距离进行有效性验证

与基于情感标签推荐相比

实验结果表明

基于音乐内涵空间推荐音乐能较好地满足用户的情感需求。

Abstract

Focusing on the music affective

a concept of music connotative space is introduced. The semantic polar scale is examined and the connotative space is constructed by musical strength

speed and intensity for music affective recommendation. The music connotative space bridges the gap between the objective levels of audio frequency characters and subject emotional rang inequality

and deals with the difference of music emotional label due to different listener experiences. According to 10 672 music records. the effectiveness of the proposed music connotative space is validated in terms of Kendall's tau distance. The music connotation space outperforms emotion label in music classification for music affective recommendation.

关键词

Keywords

references

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.

LIU Dan, LU Lie, ZHANG Hongjiang. Automatic mood detection from acoustic music data[C]∥Proceedings of the International Symposium on Music Information Retrieval. Baltimore, MD, USA: The Johns Hopkins University Press, 2003: 81-87.

LI Tao, OGIHARA M. Content-based music similarity search and emotion detection[C]∥Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway, NJ, USA: IEEE, 2004: 705-708.

YANG Dan, LEE W S. Music emotion identification from lyrics[C]∥Proceedings of the 11th IEEE International Symposium on Multimedia. Piscataway, NJ, USA: IEEE, 2009: 624-629.

夏云庆, 杨莹, 张鹏洲, 等. 基于情感向量空间模型的歌词情感分析[J]. 中文信息学报, 2010, 24(1): 99-103.

XIA Yunqing, YANG Ying, ZHANG Pengzhou, et al. Lyric-based song sentiment analysis by sentiment vector space model[J]. Journal of Chinese Information Processing, 2010, 24(1): 99-103.

李静, 林鸿飞, 李瑞敏. 基于情感向量空间模型的歌曲情感标签预测模型[J]. 中文信息学报, 2012, 26(6): 45-50.

LI Jing, LIN Hongfei, LI Ruimin, et al. Sentiment vector space model based musical emotion tag prediction[J]. Journal of Chinese Information Processing, 2012, 26(6): 45-50.

SU Xiaoyuan, KHOSHGOFTAAR T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, 2009: 1-19.

GOVINDARAJULU Z. Rank correlation methods[J]. Technometrics, 1992, 34(1): 108.

HUQ A, BELLO J P, ROWE R, et al. Automated music emotion recognition: a systematic evaluation[J]. Journal of New Music Research, 2010, 39(3): 227-244.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Diffusion Characteristics and Influencing Factors in Case of Hydrogen Leakage from Fuel Cell Vehicles Running in Tunnels

I/O Performance Modeling and Prediction Method for Large-Scale Parallel Applications Using Machine Learning

Review of the Application of EBSILON Software in Combined Heat and Power System Optimization

Electrothermal Coupling Research on the Evolutionary Law of Anti-Corona Performance for High-Altitude Air-Cooled Generators

Integrated and Joint Design Optimization of Centrifugal Compressor Impeller and Casing Treatment at Multiple Working Points

Related Author

No data

Related Institution

No data

⁰