Affective content analysis of music emotion through EEG
Affective content analysis of music emotion through EEG
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DOI：
10.1007/s00530-017-0542-0
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发表时间：
2018-03-01
影响因子：
3.9
通讯作者：
Chiu, Yi-Shiuan
中科院分区：
文献类型：
--
作者：
Hsu, Jia-Lien;Zhen, Yan-Lin;Chiu, Yi-Shiuan
音乐对象的情感识别是音乐信息检索领域中一个很有前途的重要研究课题。通常，音乐情感识别可以被认为是一个训练/分类问题。然而，即使给定一个基准（具有地面真值的训练数据）并使用有效的分类算法，音乐情感识别仍然是一个具有挑战性的问题。大多数先前的相关工作仅关注声学音乐内容而不考虑个体差异（即，个性化问题）。此外，对情绪的评估通常是自我报告的（例如，情感标签），这可能引入不准确和不一致。脑电图（EEG）是一种非侵入性的脑机接口，它允许外部机器无需手术即可感知来自大脑的神经生理信号。这种从中枢神经系统捕获的非侵入性EEG信号已被用于探索情绪。提出了一种基于证据的个性化音乐情感识别模型。在模型构建和个性化适应的训练阶段，基于IADS（国际情感数字化声音系统，一组用于情感和注意力实验研究的声学情感刺激），我们构建了两个预测和通用模型（“标准化组与情绪的EEG记录”）和（“音乐音频内容与情绪”）。这两个模型都是由人工神经网络训练的。然后，我们收集一个主题的EEG记录时，听选定的IADS样本，并应用确定主题的情绪向量。利用类属模型和相应的个体差异，通过射影变换构造个性化模型H。在测试阶段，给定音乐对象，处理步骤为：（1）从音乐音频内容中提取特征，（2）应用以计算唤醒-效价情感空间中的向量，以及（3）应用变换矩阵H以确定个性化情感向量。此外，相对于一个温和的音乐对象，我们应用一个滑动窗口上的音乐对象，以获得一系列的个性化的情感向量，其中这些预测的向量将被拟合和组织为一个情感线索，揭示动态的情感内容的音乐对象。实验结果表明该方法是有效的。
Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models ("EEG recordings of standardized group vs. emotions") and ("music audio content vs. emotion"). Both models are trained by an artificial neural network. We then collect a subject's EEG recordings when listening the selected IADS samples, and apply the to determine the subject's emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.