We installed some Silent Coat sound deadening mats on the van over the weekend. We weren’t really sure how effective they’d be at reducing road noise, body noise, or at stopping outside sounds from coming in and vice versa. So we tried a semi-scientific test, to at least get a sense of how they behave while we’re driving the van.
I used a Zoom H2N portable recorder in the front passenger seat of the cab to record the ambient noise as we drove roughly the same 10 minute route n light traffic up to speeds of 40 mph.
I also installed a noise metering app on my phone – Decibel 10 – and recorded the data from the same position.
We recorded both times with an empty van, stripped of floor and ply lining. Obviously, the ‘before’ take is before we installed the sound deadening mats, the ‘after’ take is after.
Here are the two audio recordings from the Zoom recorder loaded into Logic. There has been no treatment of these audio files; in particular, no normalisation. I just trimmed off some excess sound at the beginning and end. Green is before, blue is after:
Here are the decibel readings from Decibel 10 over a 5 minute period (the drive was about 10 minutes, the app only saves the last 5 minutes of data). Again, green is before, blue is after. The spiky pale coloured lines are the raw data; I also plotted a moving average to smooth it out to something more legible. Those are the darker, smoother lines.
Finally, I also saved the audio files from the Zoom recorder as MP3s and listened back to them. You can download them here if you’d like to do the same:
Conclusions and caveats
My reading of all this is that there’s not much difference. The graphs and amplitude spectra look much of a muchness. The audio files sound very similar; I would only say that maybe the noise in the ‘after’ recording is of a lower overall frequency.
I can’t speak for other benefits or effects based on this experiment.
As a scientific experiment, this leaves much to be desired, so I’d be interested to know if others have tried similar tests, or could suggest ways we could improve our methodology, or interpret the results differently.
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