# Intelligent Whistling System of Rail Train Based on YOLOv4 and U-Net
## 1 Introduction  
A railway signal is composed of a visual signal and an auditory signal. As the main auditory signal, the train whistle is used to communicate with the train operation management and reduce traffic accidents. Each country has issued regulations on the technical management of train operation, which specify the type of train whistle. Train drivers must honk in strict accordance with the regulations [1; 2; 3]. However, in the process of driving, it is impossible for drivers to maintain a high degree of concentration at all times. In addition, the sight may be blocked due to bad weather and other factors, so train traffic accidents occur from time to time. For example, 26 people were killed, and more than 130 were injured in the collision between a passenger train and a freight train in Los Angeles on 12 September 2008. On 4 December 2011, a train and a car crash occurred in west-central France, which resulted in four deaths, including three children [4]. These tragic accidents exposed that the train whistle and brake system relying solely on human control has certain defects. In terms of train noise control, current research and application are mainly divided into two categories [5; 6]. One is to reduce the excitation intensity of the noise vibration source so as to reduce the acoustic energy radiated outward. For example, [PERSON] et al. proved that the application of dynamic damper theory to low-floor railway trains could effectively reduce the vertical abnormal vibration of trains by establishing a vertical mathematical model and three-dimensional dynamic simulation model [7]; The other method is to increase the attenuation of noise by building noise barriers and other measures to reduce the impact on residential areas along the line. For example, [PERSON] et al. studiedthe dynamic response of high noise barriers during the passage of high-speed trains and developed a practical evaluation method for the case of increasing the running speed in the future [8]. However, considering the actual economic benefits, it is difficult to popularize this vibration-reduction and noise-reduction equipment to the whole railway line. For example, noise barriers are usually only installed on key roads, such as densely populated residential areas, while their popularity is low in sparsely populated villages. At the same time, the train whistle has the characteristics of having a high pitch and being short, and the noise pollution caused by it is much higher than the train-running noise. Therefore, it is of great significance to study how to reduce the noise brought by the whistle while maintaining its safe-sounding effect [9]. With the rapid development of artificial intelligence, deep learning is widely used in computer vision [10], speech recognition [11], and fault diagnosis [12]. The continuous improvement of various efficient algorithms and processor computing power has provided a strong driving force for the intelligent and safe driving of trains [13]. At present, a large number of scholars have applied various deep-learning models to the field of railway transportation. For example, [PERSON] et al. used a shallow information fusion convolutional neural network (SSIF-CNN) to effectively diagnose the bearing fault of a high-speed train axle box [14]. [PERSON] et al. proposed a high-precision pole number recognition framework, including high-performance cascaded CNN-based detection and recognition YOLO (DR-YOLO) and time redundancy methods, and conducted extensive experimental research on four data sets representing the conventional working environment of high-speed trains [15]. The current whistle-control methods are based on remote communication technology and database retrieval [16]. Such methods rely too much on remote communication, so they can only achieve simple volume control in designated whistling-restricted areas or stations. At the same time, due to the poor adaptability of the database, it is unable to make real-time responses to pedestrians, vehicles, and new buildings near the track.