mic1 = [2, 1.5] # position
M = 8 # number of microphones
d = 0.08 # distance between microphones
phi = 0. # angle from horizontal
max_order_design = 1 # maximum image generation used in design
shape = 'Linear' # array shape
Lg_t = 0.050 # Filter size in seconds
Lg = np.ceil(Lg_t * Fs) # Filter size in samples
delay = 0.03
# define the FFT length
N = 1024
# create a microphone array
if shape is 'Circular':
R = pra.circular2DArray(mic1, M, phi, d * M / (2 * np.pi))
elif shape is 'Poisson':
R = pra.poisson2DArray(mic1, M, d)
else:
R = pra.linear2DArray(mic1, M, phi, d)
mics = pra.Beamformer(R, Fs, N=N, Lg=Lg)
# The first signal (of interest) is singing
rate1, signal1 = wavfile.read('samples/singing_' + str(Fs) + '.wav')
signal1 = np.array(signal1, dtype=float)
signal1 = pra.normalize(signal1)
signal1 = pra.highpass(signal1, Fs)
delay1 = 0.
# the second signal (interferer) is some german speech
rate2, signal2 = wavfile.read('samples/german_speech_' + str(Fs) + '.wav')
mic1 = [2, 1.5] # position
M = 8 # number of microphones
d = 0.04 # distance between microphones
phi = 0. # angle from horizontal
max_order_design = 1 # maximum image generation used in design
shape = 'Linear' # array shape
Lg_t = 0.05 # Filter size in seconds
Lg = np.ceil(Lg_t*Fs) # Filter size in samples
delay = 0.02
# define the FFT length
N = 1024
# create a microphone array
if shape is 'Circular':
R = pra.circular2DArray(mic1, M, phi, d*M/(2*np.pi))
else:
R = pra.linear2DArray(mic1, M, phi, d)
mics = pra.Beamformer(R, Fs, N=N, Lg=Lg)
# The first signal (of interest) is singing
rate1, signal1 = wavfile.read('samples/singing_'+str(Fs)+'.wav')
signal1 = np.array(signal1, dtype=float)
signal1 = pra.normalize(signal1)
signal1 = pra.highpass(signal1, Fs)
delay1 = 0.
# the second signal (interferer) is some german speech
rate2, signal2 = wavfile.read('samples/german_speech_'+str(Fs)+'.wav')
signal2 = np.array(signal2, dtype=float)
signal2 = pra.normalize(signal2)