Which algorithm should I use for signal (sound) one class classification?

Question

Update I summarized the question and its answers here

1. My objective is to detect the presence of a given pattern in a noisy signal. I want to detect the presence of a species of insect recording the sounds with a microphone. I have previously recorded the sound of the insect in a digital format.
2. I am not trying to do voice recognition.
3. I am already using convolution between the input signal and the pattern to determine their similarity level. But I think that this technique is more suited to discrete time (i.e. digital communications, where signals occurs at fixed intervals) and to distinguish an input signal between 2 given patterns (I have only one pattern).
4. I am afraid to use neural networks, because I never used them, and I don't know if I could embed that code.

Could you please point me some other approaches, or try to convince me that my current approach still is a good idea or that neural networks may be a feasible way?

Update I already have 2 good answers, but another one would be welcome, and even rewarded.

Answer 1

A step up from convolution is dynamic time warping which can be thought of as a convolution operator that stretches and shrinks one signal to optimally match another.

Perhaps a simpler approach would be to do an FFT of the sample and determine if your insect any particular frequencies that can be filtered on.

On the more complex side, but not quite a neural network, are SVM toolkits like libsvm and svmlight that you can throw your data at.

Regardless of the path you attempt, I would spend time exploring the nature of the sound your insect makes using tools like FFT. After all, it will be easier teaching a computer to classify the sound if you can do it yourself.