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05_2_audio.py
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05_2_audio.py
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# -*- coding: utf-8 -*-
import sys
import matplotlib.pyplot as plt
import wave
import scipy.io.wavfile
import numpy
import numpy.fft
import math
import xml.etree.ElementTree as et
import os
import os.path
from lib import smooth
# http://onlamp.com/pub/a/python/2001/01/31/numerically.html?page=1
# http://xoomer.virgilio.it/sam_psy/psych/sound_proc/sound_proc_python.html
# dB: http://www.dsprelated.com/showmessage/29246/1.php
# RMS: http://www.opamp-electronics.com/tutorials/measurements_of_ac_magnitude_2_01_03.htm
# http://www.audioforums.com/forums/showthread.php?11942-extract-volume-out-of-wave-file&p=54594#post54594
def main():
os.chdir(sys.argv[1])
f_out = open("smooth_audio.txt", "w")
tree = et.parse("project.xml")
movie = tree.getroot()
path = movie.attrib["path"]
path = os.path.dirname(path)
fps = float( movie.attrib["fps"] )
os.chdir(path)
file = os.path.join(path, "audio_trimmed.wav")
print file
f = wave.open(file, "rb")
bit = f.getsampwidth() * 8
print bit, "bit" # usually: signed 16 bit [-32768, 32767]
f.close()
rate, data = scipy.io.wavfile.read(file)
print rate, "hz"
# http://en.wikipedia.org/wiki/Sound_level_meter#Exponentially_averaging_sound_level_meter
chunk = rate / 8 #25
#print max(data)
#print min(data)
max = numpy.max( numpy.absolute(data) )
"""fft = numpy.fft.rfft(data, chunk)
fft = numpy.absolute(fft)
print fft
plt.plot(fft)
plt.show()"""
data_db = numpy.array([])
data_rms = numpy.array([])
for i in range(len(data) / chunk):
values = numpy.array( data[i*chunk : (i+1)*chunk] )
# normalize [0, 1]
#values = values / 2**(bit-1)
values = values / float(max)
#values = values * float(1) # why do I need that?
# root mean square
values = numpy.power(values, 2)
rms = numpy.sqrt( numpy.mean(values) )
data_rms = numpy.append(data_rms, rms)
# decibel
db = 20 * numpy.log10( (1e-20+rms) ) #/ float(max)
data_db = numpy.append(data_db, db)
#plt.ylim(-60, 0)
#plt.plot( smooth(data_rms/numpy.max(data_rms), window_len=rate/(fps*2)), "k-" )
#plt.plot(smooth(data_db, window_len=rate/fps), "g-")
smooth_db = 1 + smooth(data_db, window_len=rate/(fps*3)) / (60.0) # [0..1]
plt.ylim(0, 1)
plt.plot(smooth_db, "g-")
for item in smooth_db:
if item < 0:
item = 0
f_out.write("%f\n" % float(item))
f_out.close()
#plt.plot(data_db)
plt.show()
#for i in range(len(data) / (rate*250)):
# plt.specgram(data[i*rate*250 : (i+1)*rate*250], Fs = rate, scale_by_freq=True, sides='default')
# plt.show()
"""def show_wave_n_spec(speech):
spf = wave.open(speech, "r")
#sound_info = spf.readframes(-1)
sound_info = spf.readframes(1000000)
sound_info = numpy.fromstring(sound_info, 'Int16')
f = spf.getframerate()
plt.subplot(211)
plt.plot(sound_info)
plt.title('Wave from and spectrogram of %s' % sys.argv[1])
plt.subplot(212)
spectrogram = plt.specgram(sound_info, Fs = f, scale_by_freq=True, sides='default')
plt.show()
spf.close()
show_wave_n_spec(fil)"""
"""
f = wave.open(file, "rb")
wav_params = f.getparams()
print wav_params
#sample_rate = wav_params[2]
sample_rate = f.getframerate()
volumes = []
chunk_size = 10 #sample_rate / 25
while True:
data_string = f.readframes(chunk_size)
unpacked = struct.unpack("%dB" % len(data_string), data_string)
if not unpacked:
break
chunk = numpy.array(unpacked)
#print chunk
chunk = pow(abs(chunk), 2)
rms = math.sqrt(chunk.mean())
#print rms
#db = 10 * math.log10(1e-20 + rms)
#print db
volumes.append(rms)
#plt.plot(volumes)
plt.specgram(volumes)
plt.show()
f.close()"""
"""
values = []
for i in range(len(data) / chunk):
x =
db = 20 * numpy.log10(1e-20 + numpy.absolute(x))
mean = numpy.mean(db)
values.append(mean)
values = numpy.array(values)
smooth_values = smooth(values, window_len=rate/5)
smooth_values2 = smooth(values, window_len=rate/10)
plt.ylim(-100, 100)
plt.plot(smooth_values)
plt.plot(smooth_values2, "r-")
plt.show()
"""
"""import numpy.fft
spectrum = numpy.fft.fft(data[:10000])
frequencies = numpy.fft.fftfreq(len(data[:10000]))
plt.plot(frequencies,spectrum)
plt.show()"""
# #########################
if __name__ == "__main__":
main()
# #########################