1 Sep 2008 17:41
Re: Create a spectrogram from a waveform
Jeff Lyon <jeff.lyon <at> cox.net>
2008-09-01 15:41:52 GMT
2008-09-01 15:41:52 GMT
I tried to run the spectrogram.py example and I appear to be having configuration problems. I have installed the latest enthought distro, but the enable module can seem to find it's api component. Any thoughts?
~ jeff$ python
Enthought Python Distribution (2.5.2001) -- http://code.enthought.com
Python 2.5.2 |EPD 2.5.2001| (r252:60911, Jul 1 2008, 19:18:12)
[GCC 4.0.1 (Apple Computer, Inc. build 5370)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import spectrum.py
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "spectrum.py", line 19, in <module>
from enthought.enable.api import Window
ImportError: No module named api
On Aug 30, 2008, at 6:28 PM, Peter Wang wrote:
Quoting Ed McCaffrey <ed <at> edmccaffrey.net>:I wrote a program in C# that creates a spectrogram from the waveform of a
.wav music file. I now want to port it to Python, and I want to try to use
SciPy instead of a direct port of the existing code, because I am not sure
that it is perfectly accurate, and it is probably slow.
I am having a hard time finding out how to do this with SciPy. With my
code, I had a FFT function that took an array of real and imaginary
components for each sample, and a second function taking both that produced
the amplitude. The FFT function in SciPy just takes one array.
Has anyone done this task in SciPy?
We have a realtime spectrogram plot in the Audio Spectrum example for
Chaco. (See the very last screenshot on the gallery page here:
You can see the full source code of the example here:
The lines you would be interested in are the last few:
pa = PyAudio()
stream = pa.open(format=paInt16, channels=1, rate=SAMPLING_RATE,
string_audio_data = stream.read(NUM_SAMPLES)
audio_data = fromstring(string_audio_data, dtype=short)
normalized_data = audio_data / 32768.0
return (abs(fft(normalized_data))[:NUM_SAMPLES/2], normalized_data)
Here we are using the PyAudio library to directly read from the sound
card, normalize the 16-bit data, and perform an FFT on it.
In your case, since you are reading a WAV file, you might be
interested in the zoomed_plot example:
This displays the time-space signal but can easily be modified to show
the FFT. Here is the relevant code that uses the built-in python
'wave' module to read the data:
You should be able to take the 'data' array in the wav_to_numeric
function and hand that in to the fft function.
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