Sound prediction based on footstep-induced vibrations in concrete building using a convolutional neural network

Heavy-weight impact sounds caused by footsteps are a major factor that affects acoustic comfort in concrete residential buildings. An impact monitoring system that predicts sound based on vibration could be beneficial to alter the behavior of the occupant causing excessive sound, or the stored data can be used by mediators in case of disputes to identify the sound source household and assess the disturbance. This study presents a method for predicting the actual impact sound especially footstep in the rooms of buildings. A convolutional neural network (CNN) was used as the prediction model and the signal from vibration sensors placed in the floors and walls of the room as input data. We experimentally collected a dataset and compared its performance according to the location of the vibration sensors and the resolution of the short-time Fourier transform (STFT) feature, which represents footstep-induced vibrations. The highest accuracy was achieved when the vibration signals of both the wall and floor slab were used simultaneously in the CNN model, with the frequency resolution of the STFT of 10 Hz and the window frame offset of 50 ms. The equivalent continuous A-weighted sound pressure level for 2 s was predicted with 0.99 dB as the mean absolute error, and the value of the coefficient of determination was 0.95. The performance of sound pressure level in the 63 and 500 Hz frequency bands achieved mean absolute error of 1.63–2.22 dB.