In order to accurately model the system, we needed to measure its frequency response. We blasted a 30-second sound clip of pure white noise at the window and recorded the signal measured by the detection unit. Since we knew the input of the system (the white noise) had a completely flat spectrum, the output’s spectrum should represent the frequency response. To compute the spectrum of the output (the recorded signal), we windowed portions of the signal using a Hamming window, computed the FFT’s of each windowed portion, and then averaged the FFT’s. This average FFT represents the frequency response of our system.

The plot shows some strong low-frequency vibrations in the window. We attributed these to the air-conditioning unit in the building and to other random vibrations in the environment. We also noticed that the window responded better to low frequencies than to high frequencies. This could be a result of the physical properties of the glass as well as the physical dimensions of the window.