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.
Highlights
This study presents the convolutional neural network (CNN)-based source type classifier and locator with a single microphone on inter-floor noise data obtained from two actual apartment buildings to verify the generalizability of the method, which considered important for data-driven approach
Similar to the learning-based method proposed in the previous studies [9,10], it learns responses with source type/position labels transmitted from discrete positions in the buildings to formulate the data-driven identification of inter-floor noise in reinforced concrete building using a single microphone, thereby extending the application of deep learning
Because 95.7% of the actual inter-floor noise complaints were identified as noise from floors above/below [4], these floor classifications are considered to be the main interest in real application
Summary
In multi-dwelling units, noises generated by occupants propagate through the structures and exert an unpleasant effect on neighboring occupants [1,2,3,4], which is a serious problem in major cities in Korea, where most residential buildings are multi-dwelling units [5]. It is challenging to identify the inter-floor noise traveling through multi-storey residential buildings owing to the human ears’ failures to intercept these sounds. The person who made the noise pretends not to know about it and ignores the victim’s complaints. For both cases, technical identification of inter-floor noise can provide a proper basis for settling the dispute. The method was verified on an actual dataset obtained from a campus building This method can be implemented in a personal mobile phone device and constructs a data-driven model that helps reduce failure of noise identification by the human ears with less human bias and provide a proper basis for settlement in the case the offender disregards the complaints. The method needs to be verified for many scenarios and to determine its limitations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
