In the originally published chapter 16 the first- and last name order of one of the authors was incorrect. The author’s name has been corrected as “Ghembaza, Moulay Ibrahim El-Khalil”.
Language is an effective way to express human emotions, but emotions are difficult to describe and judge with computers, so it is an important task to analyze their emotions through speech. We summarized the current situation of speech emotion recognition from five aspects: the development of speech emotion recognition, emotion description model, emotion speech database, feature extraction, and emotion recognition algorithm. By summarizing and analyzing these five aspects, we can predict the future development trend of speech emotion recognition and improve recognition accuracy combined with other information for joint analysis. In this paper, we summarized the commonly used emotion database, compared the popular attention mechanism in speech emotion recognition, and finally, proposed the prospect of speech emotion recognition.
This paper proposes a method of crowd counting. We use ResNeSt-50 as the backbone network of YOLOv3. After the backbone network, we add SPP (Spatial Pyramid Potential) and PANet (Path Aggregation Network) to enhance the receptive field of convolutional neural network and improve the accuracy of stream of people or crowd counting in real application scenarios. In the application scenario of high-density crowd counting, an improved VGG network is used to design a deep network to capture high-level semantic information. At the same time, a shallow network is constructed to detect the head blob of people far away from the camera. The deep network and the shallow network are combined to detect high-density crowd. Finally, through the effective fusion of the above two network models, the accuracy and applicability of the algorithm are further improved. It can improve the detection accuracy in the case of small number of people and occlusion, and effectively reduce the estimation error in the scene with high density crowd.
Because pedestrians are always in the active state, each target is at a different distance from the camera, resulting in a certain difference in the size of similar targets in the figure. Therefore, an infrared pedestrian detection algorithm is proposed in the paper based on Yolov4 algorithm. Aiming at the problems of low recognition rate and high background influence in infrared image downlink human small target detection, the network structure of YOLOv4 is optimized. Compared with YOLOv4 and YOLOv3, the mean Average Precision is improved by 0.53
The electrocardiogram reflects the temporal changes in the body’s cardiac potential; This is also an important technology for diagnosing cardiovascular disease, so the classification of electrocardiogram has gradually become the focus of many scholars. This paper designs an ECG classification algorithm based on convolutional neural network, which aims to automatically classify ECG using artificial intelligence algorithm. The algorithm has the characteristics of less parameters and more layers, and the classification speed is faster and has very strong real-time performance. The experimental results show that the accuracy of the algorithm reaches 98.1
