Detecting a specific pattern from a FFT in Arduino

I have an FFT output from a microphone and I want to detect a specific animal's howl from that (it howls in a characteristic frequency spectrum). Is there any way to implement a pattern recognition algorithm in Arduino to do that?

I already have the FFT part of it working with 128 samples @2kHz sampling rate.


  • lookup audio fingerprinting ... essentially you probe the frequency domain output from the FFT call and take a snapshot of the range of frequencies together with the magnitude of each freq then compare this between known animal signal and unknown signal and output a measurement of those differences.

    Naturally this difference will approach zero when unknown signal is your actual known signal

    Here is another layer : For better fidelity instead of performing a single FFT of the entire audio available, do many FFT calls each with a subset of the samples ... for each call slide this window of samples further into the audio clip ... lets say your audio clip is 2 seconds yet here you only ever send into your FFT call 200 milliseconds worth of samples this gives you at least 10 such FFT result sets instead of just one had you gulped the entire audio clip ... this gives you the notion of time specificity which is an additional dimension with which to derive a more lush data difference between known and unknown signal ... experiment to see if it helps to slide the window just a tad instead of lining up each window end to end

    To be explicit you have a range of frequencies say spread across X axis then along Y axis you have magnitude values for each frequency at different points in time as plucked from your audio clips as you vary your sample window as per above paragraph ... so now you have a two dimensional grid of data points

    Again to beef up the confidence intervals you will want to perform all of above across several different audio clips of your known source animal howl against each of your unknown signals so now you have a three dimensional parameter landscape ... as you can see each additional dimension you can muster will give more traction hence more accurate results

    Start with easily distinguished known audio against a very different unknown audio ... say a 50 Hz sin curve tone for known audio signal against a 8000 Hz sin wave for the unknown ... then try as your known a single strum of a guitar and use as unknown say a trumpet ... then progress to using actual audio clips

    Audacity is an excellent free audio work horse of the industry - it easily plots a WAV file to show its time domain signal or FFT spectrogram ... Sonic Visualiser is also a top shelf tool to use

    This is not a simple silver bullet however each layer you add to your solution can give you better results ... it is a process you are crafting not a single dimensional trigger to squeeze.