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The Pursuit of Happiness in Music: Retrieving Valence with Contextual Music Descriptors

机译:追求音乐中的幸福:用上下文音乐描述符检索价值

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In the study of music emotions, Valence is usually referred to as one of the dimensions of the circumplex model of emotions that describes music appraisal of happiness, whose scale goes from sad to happy. Nevertheless, related literature shows that Valence is known as being particularly difficult to be predicted by a computational model. As Valence is a contextual music feature, it is assumed here that its prediction should also require contextual music descriptors in its predicting model. This work describes the usage of eight contextual (also known as higher-level) descriptors, previously developed by us, to calculate happiness in music. Each of these descriptors was independently tested using the correlation coefficient of its prediction with the mean rating of Valence, reckoned by thirty-five listeners, over a piece of music. Following, a linear model using this eight descriptors was created and the result of its prediction, for the same piece of music, is described and compared with two other computational models from the literature, designed for the dynamic prediction of music emotion. Finally it is proposed here an initial investigation on the effects of expressive performance and musical structure on the prediction of Valence. Our descriptors are then separated in two groups: performance and structural, where, with each group, we built a linear model. The prediction of Valence given by these two models, over two other pieces of music, are here compared with the correspondent listeners' mean rating of Valence, and the achieved results are depicted, described and discussed.
机译:在音乐情绪的研究,均价为通常被称为情感环状模型描述的幸福音乐的评价,其规模从去伤心快乐的维度之一。然而,相关文献表明,贵重率被称为计算模型特别难以预测。随着价值是一种语境音乐特征,这里假设其预测还应在其预测模型中需要上下文音乐描述符。这项工作描述了八个上下文(也称为高级)描述符,以前由我们开发的,计算音乐中的幸福。这些描述符中的每一个使用其预测的相关系数独立地测试,其平均值的平均值,由三十五个听众估计,在一段音乐上。以下,使用该八个描述符的线性模型并将其预测的结果用于相同的音乐,并与来自文献的两个其他计算模型相比,专为音乐情绪的动态预测而设计。最后提出了关于表现性能和音乐结构对价预测的效果的初步调查。然后,我们的描述符分为两组:性能和结构,在其中,每个组,我们构建了一个线性模型。通过这两个模型,两种其他音乐件的价值的预测在这里与价值的相应听众的平均等级相比,所以所达到的结果被描绘,描述和讨论。

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