期刊
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 7, 期 2, 页码 122-135出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2015.2462841
关键词
Music information retrieval; mood prediction; social tags; semantic computing; music genre; genre-adaptive
资金
- Academy of Finland (The Finnish Centre of Excellence in Interdisciplinary Music Research)
- EPSRC programme grant Fusing Semantic and Audio Technologies for Intelligent Music Production and Consumption (FAST-IMPACt) [EP/L019981/1]
- EPSRC [EP/L019981/1] Funding Source: UKRI
- ESRC [ES/K00753X/1] Funding Source: UKRI
- Economic and Social Research Council [ES/K00753X/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/L019981/1] Funding Source: researchfish
This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements in the prediction rates are obtained for the valence dimension which is typically the most challenging core affect dimension for audio-based annotation. The specificity of genre categories is not crucial for the performance of ACTwg-SLPwg. The study also presents analytical insights into inferring a concise tag-based genre representation for genre-adaptive music mood analysis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据