Data clustering analysis of early reflections in small room

Abstract

It is common to increase the number of measurement points to improve the robustness of multipoint room equalization. However, the measurement of numerous points is extremely time-consuming and laborious. This letter analyzes the early reflections extracted from a large amount of room impulse responses using a K-means clustering algorithm, revealing that the spatial distribution of early reflections in the same cluster is not disorganized but regular and predictable. Furthermore, the results of the Monte Carlo simulation suggest that the appropriate selection of measurement positions can reduce the number of measurement points without compromising the robustness of multipoint room equalization.