aaronleese wrote:Well generally speaking, that is because whistling is a fairly sharp, monophonic signal.
Other signals are more complicated (more resonance or overtones) and so they don't have a single spike the way whistling does. Instead they have a pattern of overtones in the freq spectrum.
You could do some pattern analysis on the FFT though and get closer to identifying pitch for polyphonic signals.
aaronleese wrote:Maybe get really good at it and give melodyne some competition.
OK yeah I spent a few minutes playing with a microphone and a frequency spectrum and I totally see what you mean !!
However when you sing/whistle/play a note, there's a relationship between it's harmonics frequency : f, 2*f , 3*f and so on. Every harmonic is a "little peak" (in time domain) so in freq domain, you should have a way to use this info ?
You can always play with autocorrelation
For each pair of these partials, the algorithm ﬁnds the “smallest harmonic numbers” that would correspond to a harmonic series with these two partials in it. As an example, if the two partials occurred at 435 Hz and 488 Hz, the smallest harmonic numbers (within a certain threshold) would be 6 and 7, respectively. Each of these harmonic number pairs are then used as a hypothesis for the fundamental frequency of the signal. In the previous example, the pair of partials would correspond
to a hypothesis that the fundamental frequency of the signal is about 70 Hz
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