Source Type Classification and Localization of Inter-Floor Noise with a Single Sensor and Knowledge Transfer between Reinforced Concrete Buildings

Abstract

A convolutional neural network (CNN)-based inter-floor noise source type classifier and locator with input from a single microphone was proposed in [Appl. Sci. 9, 3735 (2019)] and validated in a campus building experiment. In this study, the following extensions are presented: (1) data collections of nearly 4700 inter-floor noise events that contain the same noise types as those in the previous work at source positions on the floors above/below in two actual apartment buildings with spatial diversity, (2) the CNN-based method for source type classification and localization of inter-floor noise samples in apartment buildings, (3) the limitations of the method as verified through several tasks considering actual application scenarios, and (4) source type and localization knowledge transfer between the two apartment buildings. These results reveal the generalizability of the CNN-based method to inter-floor noise classification and the feasibility of classification knowledge transfer between residential buildings. The use of a short and early part of event signal is shown as an important factor for localization knowledge transfer.

Keywords

• Inter-floor noise
• single sensor acoustics
• convolutional neural network
• knowledge transfer