[SciPy-user] Create a spectrogram from a waveform
Sun Aug 31 08:49:18 CDT 2008
Thanks for the replies. I think that now I am heading towards the right
direction, but I have one problem. When I run my program all I get for the
spectrogram is a solid blue graph.
The program is:
from scipy import *
from pylab import *
from wave import *
wav = open('song.wav')
length = wav.getnframes()
data = [struct.unpack('f', wav.readframes(1)) for x in range(length)]
spectrogram = specgram(data)
I tried it with a few different short clips with the same result. One of
them can be found: http://edmccaffrey.net/misc/song.wav
On Sat, Aug 30, 2008 at 9:28 PM, Peter Wang <email@example.com> wrote:
> Quoting Ed McCaffrey <firstname.lastname@example.org>:
> > 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
> > SciPy instead of a direct port of the existing code, because I am not
> > 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
> > 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:
> def get_audio_data():
> 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.
> SciPy-user mailing list
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