Applied Sciences, Vol. 13, Pages 7122: Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals

Applied Sciences, Vol. 13, Pages 7122: Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals

Applied Sciences doi: 10.3390/app13127122

Authors: Peiyuan Jin Zhiwei Si Haibin Wan Xiangrui Xiong

Since informatization and digitization came into life, audio signal emotion classification has been widely studied and discussed as a hot issue in many application fields. With the continuous development of artificial intelligence, in addition to speech and music audio signal emotion classification technology, which is widely used in production life, its application is also becoming more and more abundant. Current research on audiovisual scene emotion classification mainly focuses on the frame-by-frame processing of video images to achieve the discrimination of emotion classification. However, those methods have the problems of algorithms with high complexity and high computing cost, making it difficult to meet the engineering needs of real-time online automatic classification. Therefore, this paper proposes an automatic algorithm for the detection of effective movie shock scenes that can be used for engineering applications by exploring the law of low-frequency sound effects on the perception of known emotions, based on a database of movie emotion scene clips in 5.1 sound format, extracting audio signal feature parameters and performing dichotomous classification of shock and other types of emotions. As LFS can enhance a sense of shock, a monaural algorithm for detecting emotional scenes with impact using a subwoofer (SW) is proposed, which trained a classification model using SW monaural features and achieved a maximum accuracy of 87% on the test set using a convolutional neural network (CNN) model. To expand the application scope of the above algorithm, a monaural algorithm for detecting emotional scenes with impact based on low-pass filtering (with a cutoff frequency of 120 Hz) is proposed, which achieved a maximum accuracy of 91.5% on the test set using a CNN model.