基于RF-LCE-BiLSTM-Attention-AMSSA模型的京剧二分类

Beijing Opera Binary Classification Based on RF-LCE-BiLSTM-Attention-AMSSA Model

为了提升大数据背景下的京剧分类精度,促进国粹传播,本文在RF-BiLSTM-Attention模型基础上利用偏差惩罚交叉熵(LCE)损失函数防止过拟合,并融合自适应麻雀优化算法(AMSSA)提出RF-LCE-BiLSTM-Attention-AMSSA模型对京剧与其他音乐进行二分类。模型首先将音频文件转化为由特征构成的向量,再结合L2正则化损失和交叉熵损失作为模型的偏差惩罚交叉熵损失函数,通过神经网络进行分类训练;然后利用AMSSA优化模型超参数,将最优超参数代入模型进行京剧二分类。以来自大众音乐平台和GTZAN数据集的1500首音乐进行京剧二分类实验,对比RF-LCE-BiLSTMAttention-AMSSA模型与RNN、LSTM、BiLSTM等10种模型的分类准确率,并对LCE损失函数与AMSSA进行消融实验。实验结果表明:RF-LCE-BiLSTM-Attention-AMSSA模型的分类准确率为89.95%,比RF-BiLSTM-Attention提高了0.95个百分点;比RF-LCE-BiLSTM-Attention提高了0.28个百分点。

In order to improve the classification accuracy of Beijing Opera in the era of big data and promote the dissemination of national essence,this article uses the deviation penalty cross entropy loss function on the basis of RF-BiLSTM-Attention to prevent overfitting and integrates the Adaptive Multi-Swarm Sparrow Search Algorithm(AMSSA)to propose the RF-LCE-BiLSTM Attention-AMSSA model for binary classification of Beijing Opera and other music.The model first converts audio files into feature vectors,and then combines L2 regularization loss and Cross Entropy loss(LCE)as the deviation penalty cross entropy loss function of the model,which is trained through neural networks for classification.After that,the AMSSA is adopted to optimize the hyperparameters,and the optimal hyperparameters are applied for the binary classification of Beijing Opera.A Beijing Opera binary classification experiment was conducted on 1500 pieces of music,which come from the popular music platform and GTZAN dataset,to compare the classification accuracy of RF-LCE-BiLSTM-Attention-AMSSA model with 11 models such as RNN,LSTM,and BiLSTM,and to compare the impact of LCE loss function and AMSSA on the model.The results show that the classification accuracy of RF-LCE-BiLSTM-Attention-AMSSA model is 89.95%,which is 0.95 percentage points higher than RF-BiLSTM-Attention,and 0.28 percentage points higher than RF-LCE-BiLSTM-Attention.