In recent years, many kinds of sensors have been studied to recognize the environment, and they are used for AR and VR applications and for SLAM. Although ultrasonic signals with high directivity and high resolution are often used, there are problems such as ultrasonic exposure and grating noise at the rising edge. In this paper, we propose a new active sensing method based on audible sounds that is robust to environmental noise by combining weighting likelihood functions and standing waves. Compared to ultrasonic signals, audible sound tends to spread out, which leads to misalignment of distance estimates and loss of map consistency over time. Therefore, we derive the effective azimuth angle based on the directional characteristics of the speaker and calculate the likelihood of the presence or absence of obstacles using the observation model. In addition, we introduce occupancy grid mapping to produce a map that best explains the estimated distances. We performed real-world two-dimensional environment recognition experiments using the proposed method to detect and map surrounding obstacles, and showed the effectiveness of the method.