Indoor Object Identification based on Spectral Subtraction of Acoustic Room Impulse Response

Abstract

Object identification in the room environment is a key technique in many advanced engineering applications such as the unidentified object recognition in security surveillance, human identification and barrier recognition for AI robots. The identification technique based on the sound field perturbation analysis is capable of giving immersive identification which avoids the occlusion problem in the traditional vision-based method. In this paper, a new insight into the relation between the object and the variation of the sound field is presented. The sound field difference before and after the object locates in the environment is analyzed using the spectral subtraction based on the room impulse response. The spectral subtraction shows that the energy loss caused by the sound absorption is the essential factor which perturbs the sound field. By using the energy loss with high uniqueness as the extracted feature, an object identification technique is constructed under the classical supervised pattern recognition framework. The experiment in a real room validates that the system has high identification accuracy. In addition, based on the feature property of position insensitivity, this technique can achieve high identifying accuracy with a quite small training data set, which demonstrates that the technique has potential to be used in real engineering applications.