Hi Anthony, For pitch analysis, what technique to use depends a lot on the specific application and the expected signal.
Some considerations... Is the input monophonic (e.g. one singing voice or single pitched instrument) or polyphonic (piano, orchestra, etc.)? Does the pitch analysis need to happen in real-time (i.e. from a live source), or can it be done in a batch/non-real-time fashion (from a recording)? What frequency and time resolution do you need? How computationally-intensive can it be? Counting zero crossings is simple and efficient, but I wouldn't recommend it. It'll work fine on sine tones, but on signals with any significant energy at harmonics above the fundamental (i.e. virtually any real-world sound), it'll tend to give you pitch estimates that are multiples of the actual pitch. Low-pass filtering the input can alleviate that problem a bit. Personally I've found that for most monophonic signals, auto-correlation-based methods work best. I can send you a well-commented C++ example that I coded up a while ago. For polyphonic pitch-detection, spectral-based methods are the way to go. Typically the first stage of these is a windowed FFT, though some folks use wavelet or constant-Q transforms instead. I have a decent AS3 FFT here: http://gerrybeauregard.wordpress.com/2010/08/03/an-even-faster-as3-fft/ Even for monophonic sources, if the fundamental is substantially stronger than the other harmonics, simply using the biggest spectral peak may work. My AS3 spectrum analyzer project would be a good place to start if you want to try this approach. Code is here: http://gerrybeauregard.wordpress.com/2010/08/06/real-time-spectrum-analysis/ Live version here: http://www.audiostretch.com/spectrum/ BTW, are you working on a commercial project? If so, I'd be happy to do some consulting/contracting work! ;-) Cheers, Gerry Beauregard g.beaureg...@ieee.org On 2011-05-12 , at 04:12 , Anthony Pace wrote: > Hello list, > > I have been doing some experiments, but although my stuff is working, it > isn't optimized at all, and I would to try a reliable lib that uses zero > crossings for analysis, if one exists. > > Any suggestions? > > I am really just interested in pitch analysis with very small sample chunks. > Timber is not really necessary, so I, with my limited DSP knowledge, think > the FFT is overkill; however, I absolutely admit I could be wrong and not > seeing something important. > > Another thing is that I was thinking a good/cheap way to get rid of some low > level background noise would be to normalize all values within the time > domain with a very high gain factor, and just give max and min values for the > zero crossings( e.g +3, -3 respectively... I know the numbers would obviously > not be these ones). I know know frequency analysis would have to be > preformed in order to remove a voice, but I am thinking that this could allow > me to zero out/ignore, really low level/really high level background noise. > Should I try something different? > > Again, I have just started reading through a copy of 'DSP: a computer science > perspective', that someone gave me, and it seems like what I am talking about > would work; yet, if not, and you have experience with signals analysis, I > would appreciate the heads up. > > Thank you, > Anthony > _______________________________________________ > Flashcoders mailing list > Flashcoders@chattyfig.figleaf.com > http://chattyfig.figleaf.com/mailman/listinfo/flashcoders _______________________________________________ Flashcoders mailing list Flashcoders@chattyfig.figleaf.com http://chattyfig.figleaf.com/mailman/listinfo/flashcoders
