Genre-Adaptive Semantic Computing and Audio-Based Modelling for Music Mood Annotation
Genre-Adaptive Semantic Computing and Audio-Based Modelling for Music Mood Annotation
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DOI：
10.1109/taffc.2015.2462841
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发表时间：
2016-04-01
影响因子：
11.2
通讯作者：
Sandler, Mark
中科院分区：
文献类型：
--
作者：
Saari, Pasi;Fazekas, Gyorgy;Sandler, Mark
本研究探讨是否考虑体裁是有益的自动音乐情绪标注的核心影响效价，唤醒，和紧张，以及其他几个情绪量表。提出了采用类型自适应语义计算和基于音频的建模的新技术。一种名为ACTwg的技术采用了与情绪相关的社交标签的体裁自适应语义计算，而ACTwg-SLPwg则以体裁自适应的方式结合了语义计算和基于音频的建模。所提出的技术进行实验评估，在预测听众收视率相关的一组600流行音乐曲目跨越多种流派。结果表明，ACTwg优于语义计算技术，不利用体裁信息，和ACTwg-SLPwg优于传统的技术和其他体裁自适应的替代品。特别是，在预测率的改善获得的效价维度，这通常是最具挑战性的核心影响维度的音频为基础的注释。体裁类别的特异性对ACTwg-SLPwg的表现并不重要。该研究还提出了分析见解推断一个简洁的基于标签的体裁表示体裁自适应音乐情绪分析。
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.