Yu Han
School of Journalism and Communication, Northwest University, Xi'an, China.
Apparel and Art Design College, Xi'an Polytechnic University, Xi'an, China.
Front Psychol. 2022 Apr 21;13:872433. doi: 10.3389/fpsyg.2022.872433. eCollection 2022.
How to enable the computer to accurately analyze the emotional information and story background of characters in Qin opera is a problem that needs to be studied. To promote the artistic inheritance and cultural emotion color dissemination of Qin opera, an emotion analysis model of Qin opera based on attention residual network (ResNet) is presented. The neural network is improved and optimized from the perspective of the model, learning rate, network layers, and the network itself, and then multi-head attention is added to the ResNet to increase the recognition ability of the model. The convolutional neural network (CNN) is optimized from the internal depth, and the fitting ability and stability of the model are enhanced through the ResNet model. Combined with the attention mechanism, the expression of each weight information is strengthened. The multi-head attention mechanism is introduced in the model and a multi-head attention ResNet, namely, MHAtt_ResNet, is proposed. The network structure can effectively identify the features of the spectrogram, improve the weight information of spectrogram features, and deepen the relationship between distant information in long-time series. Through experiments, the proposed model has high emotional classification accuracy for Qin opera, and with the increase of the number of data sets, the model will train a better classification effect.
如何使计算机准确分析秦腔中人物的情感信息和故事背景是一个有待研究的问题。为促进秦腔的艺术传承和文化情感色彩传播，提出了一种基于注意力残差网络（ResNet）的秦腔情感分析模型。从模型、学习率、网络层数以及网络本身等角度对神经网络进行改进和优化，然后在ResNet中添加多头注意力以提高模型的识别能力。从内部深度对卷积神经网络（CNN）进行优化，通过ResNet模型增强模型的拟合能力和稳定性。结合注意力机制，强化各权重信息的表达。在模型中引入多头注意力机制，提出了一种多头注意力ResNet，即MHAtt_ResNet。该网络结构能够有效识别频谱图的特征，提高频谱图特征的权重信息，加深长时间序列中远距离信息之间的关系。通过实验表明，所提模型对秦腔具有较高的情感分类准确率，并且随着数据集数量的增加，模型将训练出更好的分类效果。
