def do_beamforming(self, mic_data):
""" Beamforming using Acoular """
mg, rg, st = self.get_acoular_essentials()
count = 0
#Divide audio samples as per frame rate (10fps) and do beamforming
for s_time in tqdm(
np.arange(self.start_time, self.end_time, self.interval)):
audio_data = mic_data[:,
int(s_time *
self.sample_rate):int((s_time +
self.interval) *
self.sample_rate)]
audio_data = np.transpose(audio_data)
if audio_data.shape[0] == 0:
continue
#Acoular needs audio input through .h5 file
target_file = self.outfile + '/temp.h5'
if os.path.exists(target_file):
os.remove(target_file)
with h5py.File(target_file, 'w') as data_file:
data_file.create_dataset('time_data', data=audio_data)
data_file['time_data'].attrs.__setitem__(
'sample_freq', self.sample_rate)
#.h5 file has issues with closing. Change 'ulimit' if not working
ts = TimeSamples(name=target_file)
ps = PowerSpectra(time_data=ts,
block_size=128,
window='Hanning',
overlap='50%')
bb = BeamformerEig(freq_data=ps, steer=st)
pm = bb.synthetic(self.freq_query, self.octave_band)
Lm = L_p(pm)
if count == 0:
bf_data = np.zeros(
(Lm.shape[0], Lm.shape[1],
len(
np.arange(self.start_time, self.end_time,
self.interval))))
bf_data[:, :, count] = Lm
else:
bf_data[:, :, count] = Lm
count += 1
# remove temp.h5 file after its finished
os.remove(target_file)
return bf_data, rg
def run():
from os import path
from acoular import __file__ as bpath, MicGeom, WNoiseGenerator, PointSource,\
Mixer, WriteH5, TimeSamples, PowerSpectra, RectGrid, SteeringVector,\
BeamformerBase, L_p
from pylab import figure, plot, axis, imshow, colorbar, show
# set up the parameters
sfreq = 51200
duration = 1
nsamples = duration * sfreq
micgeofile = path.join(path.split(bpath)[0], 'xml', 'array_64.xml')
h5savefile = 'three_sources.h5'
# generate test data, in real life this would come from an array measurement
mg = MicGeom(from_file=micgeofile)
n1 = WNoiseGenerator(sample_freq=sfreq, numsamples=nsamples, seed=1)
n2 = WNoiseGenerator(sample_freq=sfreq,
numsamples=nsamples,
seed=2,
rms=0.7)
n3 = WNoiseGenerator(sample_freq=sfreq,
numsamples=nsamples,
seed=3,
rms=0.5)
p1 = PointSource(signal=n1, mics=mg, loc=(-0.1, -0.1, 0.3))
p2 = PointSource(signal=n2, mics=mg, loc=(0.15, 0, 0.3))
p3 = PointSource(signal=n3, mics=mg, loc=(0, 0.1, 0.3))
pa = Mixer(source=p1, sources=[p2, p3])
wh5 = WriteH5(source=pa, name=h5savefile)
wh5.save()
# analyze the data and generate map
ts = TimeSamples(name=h5savefile)
ps = PowerSpectra(time_data=ts, block_size=128, window='Hanning')
rg = RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \
increment=0.01 )
st = SteeringVector(grid=rg, mics=mg)
bb = BeamformerBase(freq_data=ps, steer=st)
pm = bb.synthetic(8000, 3)
Lm = L_p(pm)
# show map
imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \
interpolation='bicubic')
colorbar()
# plot microphone geometry
figure(2)
plot(mg.mpos[0], mg.mpos[1], 'o')
axis('equal')
show()
h5savefile = 'three_sources.h5' # generate test data, in real life this would come from an array measurement mg = MicGeom( from_file=micgeofile ) n1 = WNoiseGenerator( sample_freq=sfreq, numsamples=nsamples, seed=1 ) n2 = WNoiseGenerator( sample_freq=sfreq, numsamples=nsamples, seed=2, rms=0.7 ) n3 = WNoiseGenerator( sample_freq=sfreq, numsamples=nsamples, seed=3, rms=0.5 ) p1 = PointSource( signal=n1, mpos=mg, loc=(-0.1,-0.1,0.3) ) p2 = PointSource( signal=n2, mpos=mg, loc=(0.15,0,0.3) ) p3 = PointSource( signal=n3, mpos=mg, loc=(0,0.1,0.3) ) pa = Mixer( source=p1, sources=[p2,p3] ) wh5 = WriteH5( source=pa, name=h5savefile ) wh5.save() # analyze the data and generate map ts = TimeSamples( name=h5savefile ) ps = PowerSpectra( time_data=ts, block_size=128, window='Hanning' ) rg = RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \ increment=0.01 ) bb = BeamformerBase( freq_data=ps, grid=rg, mpos=mg ) pm = bb.synthetic( 8000, 3 ) Lm = L_p( pm ) # show map imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \ interpolation='bicubic') colorbar() # plot microphone geometry figure(2) plot(mg.mpos[0],mg.mpos[1],'o')
from pylab import figure, subplot, imshow, show, colorbar, title # files datafile = 'example_data.h5' calibfile = 'example_calib.xml' micgeofile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_56.xml') #octave band of interest cfreq = 4000 #=============================================================================== # first, we define the time samples using the MaskedTimeSamples class # alternatively we could use the TimeSamples class that provides no masking # of channels and samples #=============================================================================== t1 = TimeSamples(name=datafile) #=============================================================================== # calibration is usually needed and can be set directly at the TimeSamples # object (preferred) or for frequency domain processing at the PowerSpectra # object (for backwards compatibility) #=============================================================================== t1.calib = Calib(from_file=calibfile) #=============================================================================== # the microphone geometry must have the same number of valid channels as the # TimeSamples object has #=============================================================================== m = MicGeom(from_file=micgeofile) #===============================================================================
# imports from acoular
import acoular
from acoular import L_p, TimeSamples, Calib, MicGeom, EigSpectra,\
RectGrid3D, BeamformerBase, BeamformerFunctional, BeamformerEig, BeamformerOrth, \
BeamformerCleansc, BeamformerCapon, BeamformerMusic, BeamformerCMF, PointSpreadFunction, BeamformerClean, BeamformerDamas
# other imports
from os import path
#from mayavi import mlab
from numpy import amax
#from cPickle import dump, load
from pickle import dump, load
# see example3
t = TimeSamples(name='example_data.h5')
cal = Calib(from_file='example_calib.xml')
m = MicGeom(from_file=path.join(\
path.split(acoular.__file__)[0], 'xml', 'array_56.xml'))
g = RectGrid3D(x_min=-0.6, x_max=-0.0, y_min=-0.3, y_max=0.3, \
z_min=0.48, z_max=0.88, increment=0.1)
f = EigSpectra(time_data=t,
window='Hanning',
overlap='50%',
block_size=128,
ind_low=5,
ind_high=15)
csm = f.csm[:]
eva = f.eva[:]
eve = f.eve[:]
from pylab import figure, subplot, imshow, show, colorbar, title # files datafile = 'example_data.h5' calibfile = 'example_calib.xml' micgeofile = path.join( path.split(acoular.__file__)[0],'xml','array_56.xml') #octave band of interest cfreq = 4000 #=============================================================================== # first, we define the time samples using the MaskedTimeSamples class # alternatively we could use the TimeSamples class that provides no masking # of channels and samples #=============================================================================== t1 = TimeSamples(name=datafile) #=============================================================================== # calibration is usually needed and can be set directly at the TimeSamples # object (preferred) or for frequency domain processing at the PowerSpectra # object (for backwards compatibility) #=============================================================================== t1.calib = Calib(from_file=calibfile) #=============================================================================== # the microphone geometry must have the same number of valid channels as the # TimeSamples object has #=============================================================================== m = MicGeom(from_file=micgeofile) #===============================================================================
object_name.configure_traits()
m = MicGeom(from_file='UCA8.xml')
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from os import path
import acoular
from acoular import L_p, Calib, MicGeom, TimeSamples, \
RectGrid, BeamformerBase, EigSpectra, BeamformerOrth, BeamformerCleansc, \
MaskedTimeSamples, FiltFiltOctave, BeamformerTimeSq, TimeAverage, \
TimeCache, BeamformerTime, TimePower, BeamformerCMF, \
BeamformerCapon, BeamformerMusic, BeamformerDamas, BeamformerClean, \
BeamformerFunctional
object_name.configure_traits()
m = MicGeom(from_file='UCA8.xml')
m = MicGeom(from_file='UCA8.xml')
g = RectGrid(x_min=-0.8, x_max=-0.2, y_min=-0.1, y_max=0.3, z=0.8, increment=0.01)
t1 = TimeSamples(name='cry_n0000001.wav')
f1 = EigSpectra(time_data=t1, block_size=256, window="Hanning", overlap='75%')
e1 = BeamformerBase(freq_data=f1, grid=g, mpos=m, r_diag=False)
fr = 4000
L1 = L_p(e1.synthetic(fr, 0))
object_name.configure_traits()
m = MicGeom(from_file='UCA8.xml')
