mirror of
https://github.com/storytold/artcraft.git
synced 2026-10-26 19:19:38 +00:00
Brandon Thomas
104 lines
3.1 KiB
Python
Executable File
104 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
#
|
|
# Includes code from:
|
|
# http://www.frank-zalkow.de/en/code-snippets/
|
|
# create-audio-spectrograms-with-python.html
|
|
|
|
import sys
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import scipy.io.wavfile as wav
|
|
from numpy.lib import stride_tricks
|
|
|
|
|
|
""" short time fourier transform of audio signal """
|
|
def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
|
|
win = window(frameSize)
|
|
hopSize = int(frameSize - np.floor(overlapFac * frameSize))
|
|
|
|
# zeros at beginning (thus center of 1st window should be for sample nr. 0)
|
|
samples = np.append(np.zeros(np.floor(frameSize/2.0)), sig)
|
|
# cols for windowing
|
|
cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1
|
|
# zeros at end (thus samples can be fully covered by frames)
|
|
samples = np.append(samples, np.zeros(frameSize))
|
|
|
|
frames = stride_tricks.as_strided(samples,
|
|
shape=(cols, frameSize),
|
|
strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
|
|
frames *= win
|
|
|
|
return np.fft.rfft(frames)
|
|
|
|
|
|
""" scale frequency axis logarithmically """
|
|
def logscale_spec(spec, sr=44100, factor=20.):
|
|
timebins, freqbins = np.shape(spec)
|
|
|
|
scale = np.linspace(0, 1, freqbins) ** factor
|
|
scale *= (freqbins-1)/max(scale)
|
|
scale = np.unique(np.round(scale))
|
|
|
|
# create spectrogram with new freq bins
|
|
newspec = np.complex128(np.zeros([timebins, len(scale)]))
|
|
for i in range(0, len(scale)):
|
|
if i == len(scale)-1:
|
|
newspec[:,i] = np.sum(spec[:,scale[i]:], axis=1)
|
|
else:
|
|
newspec[:,i] = np.sum(spec[:,scale[i]:scale[i+1]], axis=1)
|
|
|
|
# list center freq of bins
|
|
allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
|
|
freqs = []
|
|
for i in range(0, len(scale)):
|
|
if i == len(scale)-1:
|
|
freqs += [np.mean(allfreqs[scale[i]:])]
|
|
else:
|
|
freqs += [np.mean(allfreqs[scale[i]:scale[i+1]])]
|
|
|
|
return newspec, freqs
|
|
|
|
|
|
""" plot spectrogram"""
|
|
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
|
|
samplerate, samples = wav.read(audiopath)
|
|
s = stft(samples, binsize)
|
|
|
|
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
|
|
ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
|
|
|
|
timebins, freqbins = np.shape(ims)
|
|
|
|
plt.figure(figsize=(15, 7.5))
|
|
plt.imshow(np.transpose(ims), origin="lower", aspect="auto",
|
|
cmap=colormap, interpolation="none")
|
|
plt.colorbar()
|
|
|
|
plt.xlabel("time (s)")
|
|
plt.ylabel("frequency (hz)")
|
|
plt.xlim([0, timebins-1])
|
|
plt.ylim([0, freqbins])
|
|
|
|
xlocs = np.float32(np.linspace(0, timebins-1, 5))
|
|
plt.xticks(xlocs, ["%.02f" % l for l in (
|
|
(xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
|
|
ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
|
|
plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
|
|
|
|
if plotpath:
|
|
plt.savefig(plotpath, bbox_inches="tight")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.clf()
|
|
|
|
def main():
|
|
if len(sys.argv) < 2:
|
|
print("Call as ${} INPUT_FILE".format(sys.argv[0]))
|
|
infile = sys.argv[1]
|
|
|
|
plotstft(infile)
|
|
|
|
if __name__ == '__main__':
|
|
main()
|