Existing methods for determining the geometry of an enclosed space using echolocation assume the presence of a large amount of additional equipment (sound sources and receivers) in the room. This paper investigates a method for determining the geometry of enclosed spaces using sound location. The method does not assume the presence of a priori knowledge about the surrounding space. One sound source and one sound receiver were used to create and capture real impulse characteristics. A microphone was used as a sound receiver and a finger snap was used as a sound source to produce the impulse response. In this work, we used convolutional neural networks that were trained on a large dataset consisting of 48000 impulse responses and a number of room geometry parameters corresponding to them. The trained convolutional neural network was tested on the recorded impulse responses of a real room and showed accuracy ranging from 92.2 to 98.7% in estimating room size from various parameters.
Keywords: convolutional neural networks, room geometry, echolocation, impulse response, robotics, recognition, contactless methods of measuring objects, sonar, geometry prediction, virtual reality