Scipy - Audio Processing
Personally I find Python one of the best choices out there and did myself some work in area of audio identification. You are welcomed to check for instance my software for automatic identification of birds from noisy audio recordings: Ornithokrites. The program is used by Department of Conservation of New Zealand and they are happy about it. Based on this example I would like to point out several advantages of using Python:


Huge, fast developing community providing tons of libraries. SciPy provides plethora of methods for signal processing (granted, not that many and mature as Matlab). Mind though that SciPy, although one of the most important, is only one of hundreds that can help you in your endeavours. I found Aubio best for music analysis. For speech and music recognition for sure you will enjoy great number of audio features Yaafe can extract.
It's free! Once out of academia, you quickly find out that Matlab is rather expensive. And even if you can afford it, then your perspective users will not be happy about this dependency. For instance, mentioned Department of Conservation would not accept proprietary software.
Identification often requires machine learning and Python has great toolkit for it: sklearn. It is state of the art library - and easy to use. Have a look at Kaggle competitions  (machine learning) and check how many top programmers are using Python and sklearn. 
You can manage "big data". If you want to run analysis against huge networked databases of recordings, then Python has well established set of tools. I don't think Matlab / Octave  interface easily with e.g. Hadoop, although please do correct me if I am wrong. R does better this area. 
Speaking of interfacing, you can easily interface your program with a web site. This is the way I manage Ornithokrites (bird recognition): the program runs on Amazon Web Services cloud computing service. Great if you want to provide your software to other people who do not necessarily want to go through installation procedure of all required libraries.


My second choice would be R. Although not that feature-rich as Python, it has great number of useful libraries (check e.g. seewave for your applications). Installation of those on both Windows and Linux is piece of cake, which is important if you would like others to use your program. However, to my experience high-performance computing in R is more difficult - an important thing to notice if you need to do A LOT of processing and identification.

Examples of music classification in Python:


Lecture
Audio fingerprinting
Jack Minardi blog


Book Building Machine Learning Systems with Python has a chapter on music classification

Other tools (list by no means complete): Python in Music