def test_bad_wavread(self):
""" Check wavread on bad file"""
# Create a tmp audio file with non wav format, write some random data into it,
# and check it can not be opened by wavread
rfd, fd, cfilename = open_tmp_file('pysndfiletest.wav')
try:
nbuff = 22050
noise = 0.1 * N.random.randn(nbuff)
# Open the copy file for writing
format = audio_format('aiff', 'pcm16')
b = Sndfile(cfilename, 'w', format, 1, nbuff)
b.write_frames(noise)
b.close()
b = Sndfile(cfilename, 'r')
rcnoise = b.read_frames(nbuff)
b.close()
try:
rnoise = wavread(cfilename)[0]
raise Exception("wavread on non wav file succeded, expected to fail")
except ValueError, e:
pass
#print str(e) + ", as expected"
finally:
close_tmp_file(rfd, cfilename)
def test_bad_wavread(self):
""" Check wavread on bad file"""
# Create a tmp audio file with non wav format, write some random data into it,
# and check it can not be opened by wavread
rfd, fd, cfilename = open_tmp_file('pysndfiletest.wav')
try:
nbuff = 22050
noise = 0.1 * N.random.randn(nbuff)
# Open the copy file for writing
format = audio_format('aiff', 'pcm16')
b = Sndfile(cfilename, 'w', format, 1, nbuff)
b.write_frames(noise)
b.close()
b = Sndfile(cfilename, 'r')
rcnoise = b.read_frames(nbuff)
b.close()
try:
rnoise = wavread(cfilename)[0]
raise Exception("wavread on non wav file succeded, expected to fail")
except ValueError as e:
pass
#print str(e) + ", as expected"
finally:
close_tmp_file(rfd, cfilename)
def readFromAudioFile(filename, mono=False):
'''
Calls audiolab to generate Sound object from wav and aiff files.
If mono is true, returns the left channel only.
'''
if ".wav" in filename:
data, fs, encoding = wavread(filename)
elif ".aiff" in filename:
data, fs, encoding = aiffread(filename)
else:
raise ValueError("File extension not .wav or .aiff")
if (len(data.shape)==2 and mono):
return Sound(data[:,0],fs,filename)
else:
return Sound(data,fs,filename)
def test():
wavfile = "../golf_D.wav"
data, fs = wavread(wavfile)
### STFT
fftLen = 1024
win = hanning(fftLen)
step = fftLen / 8
spectrogram = abs(stft(data, win, step)[:, :fftLen / 2 + 1]).T
### 表示
fig = pl.figure()
fig.patch.set_alpha(0.)
imshow_sox(spectrogram)
pl.tight_layout()
pl.show()
def test():
wavfile = "../golf_D.wav"
data, fs = wavread(wavfile)
### STFT
fftLen = 1024
win = hanning(fftLen)
step = fftLen / 8
spectrogram = abs(stft(data, win, step)[:, : fftLen / 2 + 1]).T
### 表示
fig = pl.figure()
fig.patch.set_alpha(0.)
imshow_sox(spectrogram)
pl.tight_layout()
pl.show()
import matplotlib.pylab as plt
import numpy as np
import loudness as ln
from audiolab import wavread
#Audio loader
fs = 32000
frameSize = 32
fileToLoad = "../../wavs/pureTones/pureTone_1000Hz_40dBSPL_32000Hz.wav"
audio = ln.AudioFileCutter(fileToLoad, frameSize)
audio.initialize()
audioBank = audio.getOutput()
nFrames = audio.getNFrames()
out = np.zeros((audioBank.getNEars(), nFrames, frameSize))
for frame in range(nFrames):
audio.process()
for ear in range(audioBank.getNEars()):
out[ear, frame] = audioBank.getSignal(ear,0)
x, fs, enc = wavread(fileToLoad)
y = out.flatten()[0:x.size]
if np.allclose(y, x):
print "Test comparing result of AudioFileCutter with audiolab's wavread: successful"
else:
print "Test comparing result of AudioFileCutter with audiolab's wavread: unsuccessful"
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 18 13:00:45 2020
@author: Gulshan Rana
"""
import audiolab, scipy
a, fs, enc = audiolab.wavread('audio1.wav')
b, fs, enc = audiolab.wavread('beep.wav')
c = scipy.vstack((a,b))
audiolab.wavwrite(c, 'ab.wav', fs, enc)
d, fs, enc = audiolab.wavread('ab.wav')
e, fs, enc = audiolab.wavread('ab.wav')
f = scipy.vstack((d,e))
audiolab.wavwrite(f, 'finaud1.wav', fs, enc)
import pydbm.dictionary
import numpy as np
import numpy.lib.recfunctions as rfn
import matplotlib.pyplot as plt
import audiolab
x, fs, p = audiolab.wavread('/Users/grahamboyes/Desktop/VibesMedium_7100-80.wav')
D = pydbm.dictionary.Dictionary(fs)
S = pydbm.dictionary.SpecDictionary(fs)
dtype = 'hann'
dur = 4096
hop = 128
tmin = 0
tmax = len(x)-dur
winargs = {}
D.addSTFT(dtype, dur, 128, tmin, tmax, **winargs)
D.index()
#dtype, midicents, scales, onsets, **kwargs)
#S.addSTFT(dtype, dur, 128, tmin, tmax, **winargs)
S.addNote(dtype, 7100, [dur, 1024], [np.arange(0, tmax, hop), np.arange(0, len(x)-1024, hop/2)], **winargs)
S.index()
#signal, cmax, srr_thresh, tolmidicents, maxPeaks, dBthresh, overspec
model, res, sBook = S.mp(x.copy(), 1000, 35., 10000, 5, -80.0, 1)
audiolab.wavwrite(model, '/Users/grahamboyes/Desktop/VibesMedium_7100-80_mod.wav', fs)
n = len(y) # lungime semnal
k = arange(n)
T = n / Fs
frq = k / T # two sides frequency range
frq = frq[range(n / 2)] # one side frequency range
print "creating fft"
Y = fft(y) / n # fft computing and normalization
Y = Y[range(n / 2)]
print "creating plot"
plot(frq, abs(Y), 'r') # plotting the spectrum
xlabel('Freq (Hz)')
ylabel('|Y(freq)|')
print "returning plot"
Fs = 44100
# sampling r
x, fs, bits = audiolab.wavread('30_yards_bark_cut_3.wav')
y = x[:, 1]
lungime = len(y)
timp = len(y) / 44100.
t = np.linspace(0, timp, len(y))
print "creating linspace"
subplot(2, 1, 1)
plot(t, y)
xlabel('Time')
ylabel('Amplitude')
subplot(2, 1, 2)
plotSpectrum(y, Fs)
show()
import pydbm.data
import pydbm.dictionary
import pydbm.book
import audiolab
import numpy as np
x, fs, p = audiolab.wavread('/Users/grahamboyes/Documents/Work/project_m/harm_fof.wav')
list_of_corpora = ['/Users/grahamboyes/Documents/Work/project_m/16khz/ordinario/SoftMallet/forte', '/Users/grahamboyes/Documents/Work/project_m/16khz/ordinario/SoftMallet/piano']
S = pydbm.data.SoundDatabase(list_of_corpora)
D = pydbm.dictionary.SoundgrainDictionary(16000, S)
D.addCorpus(np.array([512, 1024, 2048, 4096]), 0)
D.addCorpus(np.array([512, 1024, 2048, 4096]), 1)
x_ = np.zeros(len(x) + max(D.atoms['duration']))
x_[0:len(x)] = x
mod, res, B = D.mp(x_.copy(), 30, 10)
audiolab.wavwrite(mod, '/Users/grahamboyes/Desktop/prawnprollip.wav', fs)
sout = '/Users/grahamboyes/Desktop/testDIF.sdif'
B.writeSDIF(sout)
out = B.synthesize()
audiolab.wavwrite(out, '/Users/grahamboyes/Desktop/prawnprollipsynth.wav', fs)
from tempfile import mkstemp
from os.path import join, dirname
from os import remove
from audiolab import wavread, wavwrite
(tmp, fs, enc) = wavread('test.wav')
if tmp.ndim < 2:
nc = 1
else:
nc = tmp.shape[1]
print "The file has %d frames, %d channel(s)" % (tmp.shape[0], nc)
print "FS is %f, encoding is %s" % (fs, enc)
fd, cfilename = mkstemp('pysndfiletest.wav')
try:
wavwrite(tmp, cfilename, fs = 16000, enc = 'pcm24')
finally:
remove(cfilename)
import os
import numpy as np
import pydbm.dictionary
import pydbm.meta
import pydbm.data
import pydbm.atom
import pydbm.utils
import matplotlib.pyplot as plt
import audiolab
import scipy.fftpack as fftpack
import scipy.signal as sig
x, fs, p = audiolab.wavread('/Users/grahamboyes/Documents/Work/sound_corpora/MUMS_converted/DVD 3/STRINGS/CELLOS/CELLO/CelA#2_4.08sec.wav')
T = pydbm.utils.MiscUtils()
H = pydbm.atom.HannGen()
w = 2048
k = 2048
hop = 256
cc = 100
pin = 0
pend = len(x) - w
imp = np.zeros(w * 4)
imp[0] = 1.
n = len(y) # lungime semnal
k = arange(n)
T = n/Fs
frq = k/T # two sides frequency range
frq = frq[range(n/2)] # one side frequency range
print "creating fft"
Y = fft(y)/n # fft computing and normalization
Y = Y[range(n/2)]
print "creating plot"
plot(frq,abs(Y),'r') # plotting the spectrum
xlabel('Freq (Hz)')
ylabel('|Y(freq)|')
print "returning plot"
Fs = 44100; # sampling r
x, fs, bits=audiolab.wavread('30_yards_bark_cut_3.wav')
y = x[:,1]
lungime=len(y)
timp=len(y)/44100.
t=np.linspace(0,timp,len(y))
print "creating linspace"
subplot(2,1,1)
plot(t,y)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y,Fs)
show()
bits.append(0)
return bits
def get_message(fsk):
message = ""
for i in range(0, len(fsk), 10):
current_byte = (fsk[i:i + 10])[1:9] # Get the middle 8 bits
pow = [2**j for j in range(8)] # Get powers of 2 for total bits
ascii = np.dot(current_byte, pow) # Get the ascii value
message += chr(ascii) # Convert ascii to char
return message
print(get_message(get_FSK(mark_freq,
space_freq))) # Print the hidden message
elif choice == 3:
message_to_hide = str(
input("Please enter the path of the encoded message to hide: "))
file_to_hide_in = str(
input("Please enter the name of the file to hide the message in: "))
a, fs, enc = audiolab.wavread(message_to_hide)
b, fs, enc = audiolab.wavread(file_to_hide_in)
c = scipy.vstack((a, b))
audiolab.wavwrite(c, 'hidden.wav', fs, enc)
print("New file saved as 'hidden.wav'")
else:
print("Your input is not correct!")
sys.exit()
import pydbm.data
import pydbm.dictionary
import numpy as np
import audiolab
x, fs, p = audiolab.wavread('../sounds/harm_fof.wav')
sdif = '/Users/grahamboyes/Documents/Work/project_m/harm_fof_shape.sdif'
list_of_corpora = ['/Users/grahamboyes/Documents/Work/sound_corpora/16k/ModernGrand/AF/ordinario_velshort/chromatic']
#Y = pydbm.data.PolygonGroup(sdif)
S = pydbm.data.SoundDatabase(list_of_corpora)
D = pydbm.dictionary.SoundgrainDictionary(fs, S)
#for y in Y.polygons:
# D.addPolygon(x.copy(), 1024, y, 2048, 256, 6)
n = S.sdif2array('/Users/grahamboyes/Desktop/ballard.mrk.sdif', ['XTRD'])['XTRD']
n = (n['time'] * fs).astype(int)
n = np.array([0, 1000, 2000, 4000, 8000, 16000, 17000, 18000, 20000, 24000])
D.addCorpus(n, 0)
x_ = np.zeros(len(x) + max(D.atoms['duration']))
x_[0:len(x)] = x
#global scalar
g = max(abs(x_)) * 0.05
import audiolab
import pydbm.dictionary
import pydbm.data
import numpy as np
target = '/Users/grahamboyes/Desktop/AMEN_stereo.wav'
outdir = '/Users/grahamboyes/Desktop'
db = ['/Users/grahamboyes/Desktop/taggedsounds/machine']
xs, fs, p = audiolab.wavread(target)
vecs = (xs[0:, 0], xs[0:, 1])
S = pydbm.data.SoundDatabase(db)
#if there is a transient model somewhere
n = S.sdif2array('/Users/grahamboyes/Desktop/AMEN_stereo.mrk.sdif', ['XTRD'])['XTRD']
n = (n['time'] * fs).astype(int)
D = pydbm.dictionary.SoundgrainDictionary(fs, S)
for i in xrange(len(db)):
D.addCorpus(n, i)
v = []
for ind, x in enumerate(vecs):
x_ = np.zeros(len(x) + max(D.atoms['duration']))
x_[0:len(x)] = x
mod, res, M = D.mp(x_.copy(), 300, 35)
v.append(mod)
import os
import numpy as np
import pydbm.dictionary
import pydbm.meta
import pydbm.data
import pydbm.atom
import pydbm.utils
import matplotlib.pyplot as plt
import audiolab
import scipy.fftpack as fftpack
import scipy.signal as sig
x, fs, p = audiolab.wavread('/Users/grahamboyes/Documents/Work/experiments_dup/experiments/test_samples/piano/MPdecomps/threePFNOF_4100_residual.wav')
T = pydbm.utils.MiscUtils()
H = pydbm.atom.HannGen()
size = 16000
coefs = T.lpc(x[16000:17024], 17)
imp = np.zeros(size)
imp[0] = 1.
h = np.random.randn(size) #* sig.lfilter([0.5, 0.7] , 1 ,np.random.randn(size))
#h = np.zeros(size)
#for k in range(1, 40):
# h += np.cos(2*np.pi*k*220./fs*np.arange(size))
from tempfile import mkstemp
from os.path import join, dirname
from os import remove
from audiolab import wavread, wavwrite
(tmp, fs, enc) = wavread('test.wav')
if tmp.ndim < 2:
nc = 1
else:
nc = tmp.shape[1]
print "The file has %d frames, %d channel(s)" % (tmp.shape[0], nc)
print "FS is %f, encoding is %s" % (fs, enc)
fd, cfilename = mkstemp('pysndfiletest.wav')
try:
wavwrite(tmp, cfilename, fs=16000, enc='pcm24')
finally:
remove(cfilename)
import audiolab
import scipy
from pylab import *
x, fs, nbits = audiolab.wavread('laser_resampled.wav')
audiolab.play(x, fs)
N = 4*fs # four seconds of audio
X = scipy.fft(x[:N])
Xdb = 20*scipy.log10(scipy.absolute(X))
f = scipy.linspace(0, fs, N, endpoint=False)
pylab.plot(f, Xdb)
pylab.xlim(0, 5000) # view up to 5 kHz
Y = X*H
y = scipy.real(scipy.ifft(Y))
"""import pymedia
import time
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
import pylab as P
f = open('manybeeps_resampled.wav', 'rb')
s=f.read()
spa = pymedia.audio.sound.SpectrAnalyzer(1, 575, 1024)
bands= spa.asBands(5, s)
from audiolab import wavread
data, fs, enc = wavread('test.wav')
filters = ['duration:[* TO 0.5]', 'samplerate:44100', 'channels:1', 'type:wav']
P = urllib.URLopener()
outdir = '/Users/grahamboyes/Desktop/hackday'
for page in range(low, high):
P.retrieve("http://www.freesound.org/api/sounds/search/?q=%s&f=%s&p=%s&api_key=%s&format=xml"%(''.join([q + ' ' for q in query]), ''.join([f + ' ' for f in filters]), page, api_key), '%s/test.xml'%outdir)
f = etree.parse('%s/test.xml'%outdir)
root = f.getroot()
for sound in root.findall('sounds'):
for resource in sound.findall('resource'):
P.retrieve(resource.find('serve').text + '?api_key=%s'%api_key, outdir + '/' + resource.find('original_filename').text)
#a decomposition using the outdir
x, fs, p = audiolab.wavread('/Users/grahamboyes/Documents/python/pythonDafx/SOUNDS/AMEN.wav')
S = pydbm.data.SoundDatabase([outdir])
n = S.sdif2array('/Users/grahamboyes/Desktop/AMEN.mrk.sdif', ['XTRD'])['XTRD']
n = (n['time'] * fs).astype(int)
D = pydbm.dictionary.SoundgrainDictionary(fs, S)
D.addCorpus(n, 0)
x_ = np.zeros(len(x) + max(D.atoms['duration']))
x_[0:len(x)] = x
mod, res, M = D.mp(x_.copy(), 300, 35)
audiolab.wavwrite(mod[0:len(x)], '%s/model.wav'%outdir, fs)
