Quick start guide
Pre-requisite
You will get optimal benefit from this App once you master basic concepts of sound level measurements: log scale, time weighting (fast vs slow), frequency weighting (dBA, dBC), … Start point can be article from wikipedia about sound level meter: https://en.wikipedia.org/wiki/Sound_level_meter
The App itself embeds some information, available top right of main page: hit ‘i’.
Some dedicated videos are available here: https://www.youtube.com/results?search_query=SPLnFFT
The main presentation video is helpful to have an overview of this App and to discover some local help information (speech bubble). Thus, please watch it once: https://youtu.be/jGVN7m3Wd1M
Selecting frequency weighting
dB(A) is the most commonly used frequency weighting. A long press is required to switch between frequency weightings. More information in the ‘i’ page.
Using external microphones
Built-in microphone is far good enough for sound level measurement purpose. Yet, you can use external microphones such as i436 from micW. In that case, specific calibration may be needed, as illustrated in this video: https://youtu.be/CRzclSnPrIM
When using external microphone, a compensation curve can be loaded from a Dropbox account. This is a bit overkill and only applies to frequency views only. More information in the ‘i’ page.
Exporting data: snapshot, mail, Dropbox
As an option (in-app purchase), you can record and export sound level measurements to a Dropbox account, from Histo view (‘RAW’). Then, using SPLnFFT viewer on Mac, or a Matlab/Octave script or a Python script on any platform, retrieve Lp slow and fast for all the measurement period. Note that this is not the audio signal itself which is exported, but sound level information over time only, with one value every 1/8th of a second.
Frequency views (e.g. FFT, PSD, 3rdO) can be exported as well, but only as a snapshot: you export what you see currently on screen. You will get a spectral view of the audio signal averaged over time. You will not get a complete recording of spectral view over time, which briefly would give files as big as audio signal files themselves.
First step to export: pause the App, select relevant tab (FFT, Histo, …) and launch export menu bottom right, as illustrated in main presentation video.
Export through HTTP get
This is very advanced feature, because you need a remote dedicated server. Some details here.
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