# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### LFS-Based Sentiment Classification Algorithm for Audovisual Scenes
#### 2.2.4 Initial Screening of Classification Models  
The experiment in this study adopts supervised learning algorithms, as each scene clip in the movie emotion scene database is labeled with corresponding emotion tags. The preliminary selected classification models consist of six traditional machine learning models and two classical neural network models. The six machine learning models are SVM classifier, random forest classifier, Bernoulli naive Bayes classifier, Gaussian naive Bayes classifier, and multinomial naive Bayes classifier. The two neural network models are backpropagation neural network (BPNN) classifier and CNN classifier. The basic principles of these main models are as follows. 1. SVM is a classical binary classification supervised learning algorithm known for its good robustness. The main objective of this algorithm is to find the optimal separating hyperplane that correctly classifies different types of data points [33; 34], as illustrated in Figure 5.
2. BPNN is a multilayer feedforward neural network trained using the error backpropagation algorithm. The learning process of the BPNN classification model consists  
Figure 5: Graphical representation of an SVM classification model separating hyperplanes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Domain** & **Frequency Domain** & **Cepstral Domain** \\ \hline \multirow{6}{*}{Zero Crossing Rate, Energy Entropy} & \multicolumn{1}{c}{Pectral\_Centroid, Tonnetz, Spectral\_Rolloff, Spectral\_Spread, Spectral\_RMS, Central\_Streak, Spectral\_Flux, Spectral\_Flatness, Spectral\_Bandwidth, Spectral\_Contrast, Chroma\_1-Chroma\_12} & \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Audio eigenvalues extracted from time domain, frequency domain, and cepstral domain in the initial screening. of two stages: forward propagation of signals and backward propagation of errors. Firstly, during forward propagation, signals pass through the input layer, hidden layer(s), and undergo nonlinear transformations to produce output signals at the output layer. Then, during backward propagation, the error signals propagate from the output layer through the hidden layer(s) and finally reach the input layer, and the weights and biases between the hidden layer and output layer are continuously adjusted and updated in the direction of gradient descent, as well as the weights and biases between the input layer and hidden layer(s). The BPNN classification model undergoes repeated learning and training until it obtains the weights and biases corresponding to the minimum error, at which point the training stops. Figure 6 illustrates the structure of a BPNN binary classification model composed of input layer, hidden layer(s), and output layer. (3) CNN models can be used not only in image recognition but also in emotion recognition from audio signals [35]. The network structure of a CNN classification model consists of four parts: input layer, convolutional layer, pooling layer, and fully connected layer. The model is trained iteratively using the backpropagation algorithm, as shown in Figure 7 illustrating the CNN model network structure. The convolutional layer, with its characteristics of local connectivity and weight sharing, simulates simple cells with local receptive fields and extracts primary visual features [36]. This reduces the number of connections in the network significantly. The pooling layer, also known as the subsampling layer, simulates complex cells by filtering and combining the primary visual features into higher-level, abstract visual features, thereby reducing the number of parameters. The fully connected layer maps the learned \"distributed feature representation\" to the sample label space, enhancing the nonlinear capability of the network and limiting the size of the network [37]. The eigenvalues of 6-channel information in 5.1 sound format movie scene clips were used to train the model. It was found that, among the selected six traditional machine learning algorithms, the SVM performed the best.