[SciPy-user] Create a spectrogram from a waveform
Sun Aug 31 13:58:23 CDT 2008
I've found what is creating the solid blue screen. That code gives a few
NaNs in the list, if I remove them then I get actual output.
However, I think that something is wrong if I am getting NaNs, and the
spectrogram just doesn't look right. Here is updated code:
from scipy import *
from pylab import *
from wave import *
wav = open('song.wav')
length = wav.getnframes()
tmp = [struct.unpack('f', wav.readframes(1)) for x in range(length)]
data = [x for x in tmp if isnan(x) == False]
spectrogram = specgram(data)
On Sun, Aug 31, 2008 at 9:49 AM, Ed McCaffrey <email@example.com> wrote:
> 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 *
> import struct
> 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 <firstname.lastname@example.org> wrote:
>> Quoting Ed McCaffrey <email@example.com>:
>> > I wrote a program in C# that creates a spectrogram from the waveform of
>> > .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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