# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
###### Abstract
However, there is currently limited research on the characteristic changes in EEG signals related to known emotion perception and the underlying mechanisms under the influence of external stimuli, particularly in the context of LFS stimulation in audiovisual scenes and their effects on rhythmic energy changes in brain functional areas. Emotion classification of audio signals has been widely discussed as a hot topic in numerous applications. Researchers have mainly focused on the study of emotion classification in speech signals and music signals [12; 13]. [PERSON] et al. [14] used a one-class-in-one neural network for speech emotion recognition, training a subnetwork for each emotion and using the output of each subnetwork to make decisions to obtain the corresponding classification results. [PERSON] et al. [15] applied the three-dimensional emotion description model (activation-evaluation-power space) to spontaneous speech emotion recognition and treated it as a standard regression prediction problem. Enthusiasm for dimensional speech emotion recognition was also sparked [16; 17; 18; 19]. Since 2010, with the widespread adoption and extensive use of machine learning, support vector machine (SVM)-based classification algorithms have become the mainstream in speech emotion recognition. Subsequently, with the rapid development of deep learning technology and the improvement in computer processing speed, convolutional neural networks (CNNs) were utilized to learn significant emotion information from acoustic features of speech, and experimental results showed that this classification model had good robustness [20]. In ref. [21], an end-to-end approach that combined CNNs with long short-term memory (LSTM) networks was proposed to directly learn emotion information from raw speech signals. In [22], the development of speech emotion recognition over the years and future trends and challenges were summarized. In recent years, the Google team introduced transformer models into the field of speech recognition. Additionally, an end-to-end speech emotion recognition architecture with stacked transformer layers was proposed in [23]. In [24], a transformer-like model that reduces memory usage and time overhead and greatly improves computational efficiency was proposed. Emotion recognition in music signals is derived from emotion recognition in speech signals, but the analysis difficulty in music signal recognition is greater. Most researchers extract time-domain, frequency-domain, and cepstrum-domain eigenvalues related to emotion information from music signals, and use these eigenvalues to train traditional machine learning and classical deep learning models to achieve the purpose of music emotion classification [25; 26; 27]. K-nearest neighbors (KNN) and SVM are commonly used models in machine learning-based music emotion recognition. The KNN regression algorithm is affected by the dataset samples and the value of K, which may result in overfitting or underfitting during the model training process. SVM can handle both linear and nonlinear problems, and it separates different emotion music data samples by finding an optimal hyperplane in a high-dimensional space to achieve emotion recognition. The purpose of deep learning-based music emotion recognition is to solve the problem of large-scale music signal data and reduce the cost of feature extraction. In [28], a hybrid architecture combining deep learning and broad learning (BL) for music classification tasks was proposed, which improved efficiency while ensuring high performance of the classifier. Most of the current methods for emotion detection of movie scene clips in multichannel audio format focus on the parsing of video signals, which is difficult and limited in terms of recognition rate, and minor methods that use audio signals for emotion classification only use them as an auxiliary tool.