How Machines Perceive Rooms - Regions of Relevance in Room Impulse Responses

The recent surge in Extended Reality (XR) applications has sparked interest in acoustic research, particularly for enhancing immersive experiences. Room Impulse Responses (RIRs), which capture the acoustic characteristics of spaces, play a crucial role in creating plausible XR environments. Neural networks leverage RIRs for tasks such as room identification and acoustic parameter estimation. However, the decision-making process for neural networks using RIRs remains opaque. To address this, this publication applies eXplainable Artificial Intelligence (XAI) techniques, such as Layer-Wise Relevance Propagation (LRP), Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA), to identify the most relevant regions within RIRs. The techniques are applied to three independent task-dataset compositions in the area of room acoustic estimation. The findings reveal that the initial period of a RIR up to 122 ms, containing direct sound (DS) and early reflections (ER), most significantly influences network decisions.