Sound signals have been widely applied in various fields. One of the popular applications is sound localization, where the location and direction of a sound source are determined by analyzing the sound signal. In this study, two microphone linear arrays were used to locate the sound source in an indoor environment. The TDOA is also designed to deal with the problem of delay in the reception of sound signals from two microphone arrays by using the generalized cross-correlation algorithm to calculate the TDOA. The proposed microphone array system with the algorithm can successfully estimate the sound source’s location. The test was performed in a standardized chamber. This experiment used two microphone arrays, each with two microphones. The experimental results prove that the proposed method can detect the sound source and obtain good performance with a position error of about 2.0~2.3 cm and angle error of about 0.74 degrees. Therefore, the experimental results demonstrate the feasibility of the system.

This article proposes an efficient, robust, and highly accurate positioning algorithm called generalized cross-correlation – threshold-based angle clipping, which estimates the direction of a signal source based on signals received by multiple sensors. The algorithm is applied to sound localization in this study, using a circular microphone array composed of four microphones. As sound propagates through space, the distances from the sound source to each microphone vary, resulting in slight differences in the time it takes for the signal to reach each microphone. This difference is known as the arrival time delay. In typical microphone setups, abnormal delays or acoustic interference between multiple channels can cause sudden angle anomalies or drift in the localization. This study proposes a threshold-based angle clipping algorithm to address the issue of multichannel sound interference. The algorithm utilizes the generalized cross-correlation phase transform to estimate the time delay of arrival and then classifies multiple sets of angles received by the microphone array based on the root mean square error to remove problematic signals, thereby improving accuracy. Additionally, increasing the number of microphones can also improve accuracy. Experimental results demonstrate that the new algorithm, when combined with multiple microphones, effectively improves the accuracy of localization. Therefore, the proposed clipping algorithm and the research on clipping localization uncertainty reported in this article are applicable to various signal processing applications.