Oh, I also wanted to mention that over the weekend(thanks to wikipedia)
I tried a simple AMDF style approach; it seems it would be great for
very basic live tuning apps, but it does appear Gerry is correct that it
may lead to issues if used for anything else.
I also know, from what I have read, that autoCorrelation is used for
things like keeping a vocalist in tune, or producing Cher/Akon style
effects via the auto-tune vst plugin, so I am wondering if it can be
computed quite quickly? or if it is better for non-time-sensitive
analysis of pre-recorded data?
On 5/16/2011 3:51 PM, Anthony Pace wrote:
Just wanted to say thanks for the responses; especially from Gerry.
@Gerry: I think your comments about correlation have pushed me in the
right direction.
On 5/12/2011 8:49 PM, Gerry Beauregard wrote:
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
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