Detecting a low frequency cue in audio recording
You can solve this problem with a simple time based low pass filter, no need for an FFT:


Calculate the minimum integration time you want by dividing your sampling rate by the frequency you want to detect. For a 10Hz at 44100Hz, it would be 4410 samples, so let's take 4500 samples.
Then apply a low pass filter to this group of samples (choosing the right frequency cut), you will isolate the frequencies you want. I do not really know how to use them, but it should look something like this:


$$y(n) = x{(n)}+x(n-1) + ...$$


Calculate the relative power of your filtrated group of samples in dBFS (Full Scale) based on this formula:


$$p_{RMS} = \sqrt{\frac{ x_1^2 + x_2^2 + \ldots}n } $$
$$dbFS = 20\log_{10}\frac{ p_{RMS}}{p_{max}}$$
'n' behind the number of samples and 'pmax' the maximum value of a sample.

If the level you get is superior to a specific threshold of your choice (not to low, because your audio certainly have a tiny low frequency component), then there is a cue, otherwise there isn't.

As for the timestamp aspect, you can increase a variable each time you get into this low frequency detection loop, and calculate the time passed using the sampling rate. Then write an array containing the input/output time of each successful detection.

Not sure if the minimum integration time is really necessary though.