Commandline audio analysis utility? Options

[sheh]

Can anyone suggest a utility that takes an audio file as input, and can output to a text/CSV file various data (RMS, peaks, dominant frequencies, etc.) per each N ms of the input, for both time and frequency domains?

Windows is my main target, but *nix as well.

It seems SoX has a few features in this direction, but not flexible enough (e.g., data per N ms, frequency domain resolution).

[sheh]

Thanks for the suggestions.

So that’s less than half a minute for 78562 blocks. For a block length of 0.009 seconds (480101 blocks), it takes four minutes. The processor is a 1 GHz AMD 4850e, nothing extreme.

Is that a realistic concern given how trivial the processing you are doing is?

Interesting. I'm wonder if execution overhead is that trivial on a webhost, or at least, in terms of what they'd consider valid use. But then again, there's also the missing features with SoX.

Your best bet is probably MATLAB or a MATLAB Clone

Although I'd rather use something existing made for the specific job, rather than semi-program, I'll check this direction too.

As a free alternative to MATLAB I'd suggest to try Python + Numpy/Scipy. I've never used it myself, but there is also a scikit for audio processing around.

Sounds like this might need some customization, but I'll check it out. I wonder how well Python performs, but there are also potential advantages to Python for running on a shared webhost.

This post has been edited by sheh: Nov 6 2012, 04:49

[romor]

As a free alternative to MATLAB I'd suggest to try Python + Numpy/Scipy. I've never used it myself, but there is also a scikit for audio processing around.

Sounds like this might need some customization, but I'll check it out. I wonder how well Python performs, but there are also potential advantages to Python for running on a shared webhost.

In case you aren't aware, and for reference, NumPy/SciPy provide C level BLAS/LAPACK routines in Python interface, all around Numpy's multidimension (nd)array object. Many packages depend on it. Further performance boost on matrix manipulation, linear algebra and some other things, can be applied by compiling/installing (depending on platform) with Intel MKL or ATLAS libraries (roughly x30 boost, but that also is different from case to case)
Then take performance test with other suggested/possible solutions

For reading PCM WAV files, SciPy can do it. Audiolab scikit, provides additional formats through sndfile library, and was made as SciPy extension (like many others scikits)