4 ms·

I think this task is lot more about DSP, and very little ML, just Bayes classification (I will write on it later). Best book I know on DSP: The Scientist and

• simne 4y ago
  There are at least two possible approaches: 1. just adc, and work with time series. Could be easy done on modern soft/hard, but slow. 2. create Fourier transform, or use digital filters, to filter out noise (usually by filter out some frequencies) and get some sort of time-frequency table. Than easy match your sound by Bayes, with simple formula, something like: result = k[0]f[0]+k[1]f[1]+...+k[n]*f[n], f - frequency amplitude from FFT. Than just if (result > 0.8): matched = true. Just need to find right coefficients. May be, you will need to do 2-dimension matching, with sequence of sequential FFTs, this is not much harder.