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...