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首页> 外文期刊>Autonomous Mental Development, IEEE Transactions on >What Strikes the Strings of Your Heart?–Multi-Label Dimensionality Reduction for Music Emotion Analysis via Brain Imaging
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What Strikes the Strings of Your Heart?–Multi-Label Dimensionality Reduction for Music Emotion Analysis via Brain Imaging

机译：是什么打动了您的心弦？–通过大脑成像降低音乐情感分析的多标签维度

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摘要

After 20 years of extensive study in psychology, some musical factors have been identified that can evoke certain kinds of emotions. However, the underlying mechanism of the relationship between music and emotion remains unanswered. This paper intends to find the genuine correlates of music emotion by exploring a systematic and quantitative framework. The task is formulated as a dimensionality reduction problem, which seeks the complete and compact feature set with intrinsic correlates for the given objectives. Since a song generally elicits more than one emotion, we explore dimensionality reduction techniques for multi-label classification. One challenging problem is that the hard label cannot represent the extent of the emotion and it is also difficult to ask the subjects to quantize their feelings. This work tries utilizing the electroencephalography (EEG) signal to solve this challenge. A learning scheme called EEG-based emotion smoothing () and a bilinear multi-emotion similarity preserving embedding (BME-SPE) algorithm are proposed. We validate the effectiveness of the proposed framework on standard dataset CAL-500. Several influential correlates have been identified and the classification via those correlates has achieved good performance. We build a Chinese music dataset according to the identified correlates and find that the music from different cultures may share similar emotions.
机译：经过20年的广泛心理学研究，已经确定了一些可以唤起某些情感的音乐因素。但是，音乐与情感之间关系的潜在机制仍未得到解答。本文旨在通过探索系统和定量的框架来寻找音乐情感的真正关联。该任务被表述为降维问题，该问题针对给定目标寻求具有内在关联的完整而紧凑的特征集。由于一首歌通常会引起一种以上的情感，因此我们探索了降维技术以进行多标签分类。一个具有挑战性的问题是，硬标签不能代表情感的程度，也很难要求受试者量化他们的感受。这项工作试图利用脑电图（EEG）信号来解决这一挑战。提出了一种基于脑电图的情感平滑学习方法和一种双线性多情感相似性嵌入算法（BME-SPE）。我们在标准数据集CAL-500上验证了所提出框架的有效性。已经确定了几个有影响力的相关因素，并且通过这些相关因素进行的分类取得了良好的效果。我们根据识别出的相关性建立了一个中国音乐数据集，发现来自不同文化背景的音乐可能具有相似的情感。

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