Luis Garrido-4 wrote:
The biggest problem is defining what is 'similar' . Then how 'similar'
you want to get.
If you want to find an exact replica of the model sample within the
larger audio file a cross-correlation analysis is very simple and
might yield good enough results.
If you want a system with a better generalization ability, perhaps you
can extract certain parameters of the signal and feed them to some
classifying system. You could for instance use a set of model samples
to train a neural network and see how it reacts to the whole file.
There are libraries that could help you with both approaches.
But you need a good definition of the problem you want to solve and
bear in mind that what seems simple for your human brain might be
extremely complex to replicate artificially. It depends on what kind
of results are you demanding of your system. You need also a solid
background in digital signal processing. There is some general
literature on the subject available on the net.
There is a lot of literature on automated signal classification since
it is a rather useful feature to have (voice recognition is a prime
example): books, IEEE papers...
I do not want to find an exact replica of the model sample. I am definitely
more interested in the second approach you describe - extract certain
parameters of the model signal and then find something with similar values
for those parameters in the second.
I am hoping to find a library that might abstract away some of the low level
theory for me. What libraries did you have in mind?
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