# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 1 Introduction  
The screen-based simulation is becoming popular for various sports as it requires less play time and space in any weather condition [1; 2]. For example, various golf simulators have been developed mainly for pleasure [3]. Active studies have been conducted to track ball motion as it is a core technique for screen-based sport simulations. Most current commercial simulators are based on a computer vision technique using high-speed cameras [4; 5] or a radar-based technique using the doppler effect [6]. However, these systems have a limited range to locate a ball position in three-dimensional (3D) space. For example, computer vision-based systems require a user to place a ball in the field of view of the cameras. This restricts the ball placement to a small area on the ground and is not suitable for active sports such as soccer or baseball. The radar-based systems require a ball flying over a long distance (i.e., several meters or more) for accurate measurement [7], which is not suitable for indoor simulation. On the other hand, acoustic source localization provides more flexible placement and can be used to develop an indoor system for screen-based sports simulation. The technique of acoustic location was first utilized as an air defensive device during World War I with the aim of tracking the position of enemy aircraft. In recent years, acoustic source localization has been adopted in various industrial fields such as sonar [8], car repair service [9], sports simulators [10], and musical instruments [11]. Different types of localization estimation methods have been developed for each purpose. The methods of locating an acoustic source is divided into two categories according to the acoustic function type: parametric and nonparametric. Acoustic holography [12] is one of the nonparametric methods that detects not only the position of the acoustic source but also the characteristics of the acoustic field. However, it requires a lengthy calculation time and many microphones to locate the source. On the other hand, the parametric method finds the origin of the acoustic source faster than the nonparametric method by using the signal parameters with a small number of sensors. These methods are classified as a frequency domain approach and a delay and sum beamforming (DSBF) method using the concept of the time difference of arrival (TDOA) in a temporal domain. [PERSON] et al. proposed a method that estimates the direction and distance based on the frequency domain [13]. However, it was difficult to distinguish the background noises from impulsive sources such as gunshots, clapping, or hitting a ball. On the other hand, an approach based on TDOA estimates the source position using the time difference of the sound signals from a small number of samples. [PERSON] et al. proposed a technique to locate the source in 3D space by using a set of microphones arranged in a spherical form [9]. However, their system required many expensive microphones and careful placement, which made it difficult to use for a sports simulation. Recently, a deep neural network was introduced to locate an acoustic source from a noisy and reverberant environment [14; 15]. However, it demanded a large amount of training data in advance and was not effective with acoustic data that has a very short duration. The main use of the proposed system is to estimate the ball position from the impact sound in an indoor sports simulation. For this, we utilized the TDOA technique to recognize an instant length of the feature source such as the impulsive sound of striking a ball, which only lasts milliseconds. [PERSON] and [PERSON] adopted the TDOA-based technique to estimate the position and direction of an acoustic source using wavefront models based on the distances and degrees between the acoustic source candidates and microphones [16]. In their approach, a DSBF method with many two-dimensional (2D) planes was used to estimate an accurate source position in 3D space. [PERSON] et al.