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

doi:10.3969/j.issn.1006-2475.2024.11.002

Abstract

Abstract: 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.

Key words: , Beijing Opera, BiLSTM, cross entropy, AMSSA, binary classification

CLC Number:

TP183

GONG Yicheng1, 2, LIU Qing1, 2. Beijing Opera Binary Classification Based on RF-LCE-BiLSTM-Attention-AMSSA Model [J]. Computer and Modernization, 2024, 0(11): 7-12.

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