def shoebox_rir(room_dim, source, mic):
# Some simulation parameters
Fs = 8000
t0 = 1.0 / (Fs * np.pi * 1e-2) # starting time function of sinc decay in RIR response
absorption = 0.90
max_order_sim = 10
# create a microphone array
R = pra.linear2DArray(mic, 1, 0, 1)
mics = pra.Beamformer(R, Fs)
# create the room with sources and mics
room1 = pra.Room.shoeBox2D(
[0, 0], room_dim, Fs, t0=t0, max_order=max_order_sim, absorption=absorption, sigma2_awgn=0
)
# add source and interferer
room1.addSource(source)
room1.addMicrophoneArray(mics)
room1.compute_RIR()
h = room1.rir[0][0]
return h
def shoebox_rir(room_dim, source, mic):
# Some simulation parameters
Fs = 8000
t0 = 1./(Fs*np.pi*1e-2) # starting time function of sinc decay in RIR response
absorption = 0.90
max_order_sim = 10
# create a microphone array
R = pra.linear2DArray(mic, 1, 0, 1)
mics = pra.Beamformer(R, Fs)
# create the room with sources and mics
room1 = pra.Room.shoeBox2D(
[0,0],
room_dim,
Fs,
t0 = t0,
max_order=max_order_sim,
absorption=absorption,
sigma2_awgn=0)
# add source and interferer
room1.addSource(source)
room1.addMicrophoneArray(mics)
room1.compute_RIR()
h = room1.rir[0][0]
return h
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')
signal2 = np.array(signal2, dtype=float)
signal2 = pra.normalize(signal2)
signal2 = pra.highpass(signal2, Fs)
delay2 = 1.
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)
signal2 = pra.highpass(signal2, Fs)
delay2 = 1.
