Personally, I would not view this as a Perl question, nor as a problem best solved using Perl -- except for building any sort of "wrapper" utility that would make it easier to use the existing tools that are available in C and C++.

For example, http://www.isip.msstate.edu/ has a fairly comprehensive set of signal processing tools (including an HMM toolkit). These tend to prefer raw pcm data so you can use SoX to strip the WAV headers (it also does a lot of other useful stuff -- you need it anyway).

A lot will depend on the scope and actual nature of your project: how many files to compare, what criteria define "same" vs. "not same", how confusable the samples are on these criteria, what error rate is acceptable. If you're looking for cases of two files that replicate the same portion of a single digital source with little or no alteration, then DSP approaches are likely succeed quite well -- but any other condition will have a measurable error rate on both "same" and "not-same" decisions.

Another approach to consider, if the job allows it, would be to build a Perl/Tk interface that makes it very easy, fast and efficient for a human to compare the audio files and make the decisions.

Update: It's not at all clear to me that HMM's are appropriate for classifying music data. The first thing to try should probably just be comparing DFT vectors, both "narrow band" (long analysis window) and "wide band" (short window). I believe the ISIP toolkit includes a vector quantization process, which will make the statistical assessments easier.

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