def comparePlot(signal1, signal2, Fs, fft_size=512, norm=False, equal=False, title1=None, title2=None):
import matplotlib.pyplot as plt
td_amp = np.maximum(np.abs(signal1).max(), np.abs(signal2).max())
if norm:
if equal:
signal1 /= np.abs(signal1).max()
signal2 /= np.abs(signal2).max()
else:
signal1 /= td_amp
signal2 /= td_amp
td_amp = 1.
plt.subplot(2,2,1)
plt.plot(np.arange(len(signal1))/float(Fs), signal1)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title1 is not None:
plt.title(title1)
plt.subplot(2,2,2)
plt.plot(np.arange(len(signal2))/float(Fs), signal2)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title2 is not None:
plt.title(title2)
import stft
import windows
eps = constants.get('eps')
F1 = stft.stft(signal1, fft_size, fft_size / 2, win=windows.hann(fft_size))
F2 = stft.stft(signal2, fft_size, fft_size / 2, win=windows.hann(fft_size))
# try a fancy way to set the scale to avoid having the spectrum
# dominated by a few outliers
p_min = 1
p_max = 99.5
all_vals = np.concatenate((dB(F1+eps), dB(F2+eps))).flatten()
vmin, vmax = np.percentile(all_vals, [p_min, p_max])
cmap = 'jet'
interpolation='sinc'
plt.subplot(2,2,3)
stft.spectroplot(F1.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
plt.subplot(2,2,4)
stft.spectroplot(F2.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
def comparePlot(signal1, signal2, Fs, fft_size=512, norm=False, equal=False, title1=None, title2=None):
import matplotlib.pyplot as plt
td_amp = np.maximum(np.abs(signal1).max(), np.abs(signal2).max())
if norm:
if equal:
signal1 /= np.abs(signal1).max()
signal2 /= np.abs(signal2).max()
else:
signal1 /= td_amp
signal2 /= td_amp
td_amp = 1.
plt.subplot(2,2,1)
plt.plot(np.arange(len(signal1))/float(Fs), signal1)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title1 is not None:
plt.title(title1)
plt.subplot(2,2,2)
plt.plot(np.arange(len(signal2))/float(Fs), signal2)
plt.axis('tight')
plt.ylim(-td_amp, td_amp)
if title2 is not None:
plt.title(title2)
from constants import eps
import stft
import windows
F1 = stft.stft(signal1, fft_size, fft_size / 2, win=windows.hann(fft_size))
F2 = stft.stft(signal2, fft_size, fft_size / 2, win=windows.hann(fft_size))
# try a fancy way to set the scale to avoid having the spectrum
# dominated by a few outliers
p_min = 1
p_max = 99.5
all_vals = np.concatenate((dB(F1+eps), dB(F2+eps))).flatten()
vmin, vmax = np.percentile(all_vals, [p_min, p_max])
cmap = 'jet'
interpolation='sinc'
plt.subplot(2,2,3)
stft.spectroplot(F1.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
plt.subplot(2,2,4)
stft.spectroplot(F2.T, fft_size, fft_size / 2, Fs, vmin=vmin, vmax=vmax,
cmap=plt.get_cmap(cmap), interpolation=interpolation)
def spectrum(signal, Fs, N):
import stft
import windows
F = stft.stft(signal, N, N / 2, win=windows.hann(N))
stft.spectroplot(F.T, N, N / 2, Fs)
def __init__(self, N, fs, hop=None, analysis_window=None,
synthesis_window=None, channels=1, transform='numpy'):
"""
Constructor for STFT class.
Parameters
-----------
N : int
number of samples per frame
fs : float
Sampling frequency.
hop : int
hop size
wA : numpy array
analysis window
wS : numpy array
synthesis window
channels : int
number of signals
"""
# initialize parameters
self.N = N # number of samples per frame
self.fs = fs
self.D = channels # number of channels
if hop is not None: # hop size
self.hop = hop
else:
self.hop = self.N/2
self.hop = int(np.floor(self.hop))
# analysis window
if analysis_window is not None:
self.analysis_window = analysis_window
elif analysis_window is None and self.hop ==self.N/2:
self.analysis_window = windows.hann(self.N)
else:
self.analysis_window = None
# synthesis window
if synthesis_window is not None:
self.synthesis_window = synthesis_window
elif synthesis_window is None and self.hop ==self.N/2:
self.synthesis_window = None # rectangular window
else:
self.synthesis_window = None
# create DFT object
self.transform = transform
self.nfft = self.N
self.nbin = self.nfft // 2 + 1
self.freq = np.linspace(0,self.fs/2,self.nbin)
self.dft = DFT(nfft=self.nfft,fs=self.fs,num_sig=self.D,
analysis_window=self.analysis_window,
synthesis_window=self.synthesis_window,
transform=self.transform)
self.fft_out_buffer = np.zeros(self.nbin, dtype=np.complex64)
# initialize filter + zero padding --> use set_filter
self.zf = 0; self.zb = 0
self.H = None # filter frequency spectrum
# state variables
self.num_frames = 0 # number of frames processed so far
self.n_state = self.N - self.hop
# allocate all the required buffers
self.make_buffers()
def process(self):
if (self.signals is None or len(self.signals) == 0):
raise NameError('No signal to beamform')
if self.processing is 'FrequencyDomain':
# create window function
win = np.concatenate((np.zeros(self.zpf),
windows.hann(self.L),
np.zeros(self.zpb)))
# do real STFT of first signal
tfd_sig = stft.stft(self.signals[0],
self.L,
self.hop,
zp_back=self.zpb,
zp_front=self.zpf,
transform=np.fft.rfft,
win=win) * np.conj(self.weights[0])
for i in xrange(1, self.M):
tfd_sig += stft.stft(self.signals[i],
self.L,
self.hop,
zp_back=self.zpb,
zp_front=self.zpf,
transform=np.fft.rfft,
win=win) * np.conj(self.weights[i])
# now reconstruct the signal
output = stft.istft(
tfd_sig,
self.L,
self.hop,
zp_back=self.zpb,
zp_front=self.zpf,
transform=np.fft.irfft)
# remove the zero padding from output signal
if self.zpb is 0:
output = output[self.zpf:]
else:
output = output[self.zpf:-self.zpb]
elif self.processing is 'TimeDomain':
# go back to time domain and shift DC to center
tw = np.sqrt(self.weights.shape[1])*np.fft.irfft(np.conj(self.weights), axis=1)
tw = np.concatenate((tw[:, self.N/2:], tw[:, :self.N/2]), axis=1)
from scipy.signal import fftconvolve
# do real STFT of first signal
output = fftconvolve(tw[0], self.signals[0])
for i in xrange(1, len(self.signals)):
output += fftconvolve(tw[i], self.signals[i])
elif self.processing is 'Total':
W = np.concatenate((self.weights, np.conj(self.weights[:,-2:0:-1])), axis=1)
W[:,0] = np.real(W[:,0])
W[:,self.N/2] = np.real(W[:,self.N/2])
F_sig = np.zeros(self.signals.shape[1], dtype=complex)
for i in xrange(self.M):
F_sig += np.fft.fft(self.signals[i])*np.conj(W[i,:])
f_sig = np.fft.ifft(F_sig)
print np.abs(np.imag(f_sig)).mean()
print np.abs(np.real(f_sig)).mean()
output = np.real(np.fft.ifft(F_sig))
return output
def process(self, FD=False):
if self.signals is None or len(self.signals) == 0:
raise NameError('No signal to beamform')
if FD is True:
# STFT processing
if self.weights is None and self.filters is not None:
self.weightsFromFilters()
elif self.weights is None and self.filters is None:
raise NameError('Beamforming weights or filters need to be computed first.')
# create window function
win = np.concatenate((np.zeros(self.zpf),
windows.hann(self.L),
np.zeros(self.zpb)))
# do real STFT of first signal
tfd_sig = stft.stft(self.signals[0],
self.L,
self.hop,
zp_back=self.zpb,
zp_front=self.zpf,
transform=np.fft.rfft,
win=win) * np.conj(self.weights[0])
for i in xrange(1, self.M):
tfd_sig += stft.stft(self.signals[i],
self.L,
self.hop,
zp_back=self.zpb,
zp_front=self.zpf,
transform=np.fft.rfft,
win=win) * np.conj(self.weights[i])
# now reconstruct the signal
output = stft.istft(
tfd_sig,
self.L,
self.hop,
zp_back=self.zpb,
zp_front=self.zpf,
transform=np.fft.irfft)
# remove the zero padding from output signal
if self.zpb is 0:
output = output[self.zpf:]
else:
output = output[self.zpf:-self.zpb]
else:
# TD processing
if self.weights is not None and self.filters is None:
self.filtersFromWeights()
elif self.weights is None and self.filters is None:
raise NameError('Beamforming weights or filters need to be computed first.')
from scipy.signal import fftconvolve
# do real STFT of first signal
output = fftconvolve(self.filters[0], self.signals[0])
for i in xrange(1, len(self.signals)):
output += fftconvolve(self.filters[i], self.signals[i])
return output
import Room as rg import beamforming as bf from constants import eps from stft import stft, spectroplot import windows import utilities as u # Spectrogram figure properties figsize=(7.87, 1.65) # figure size figsize2=(7.87, 1.5*1.65) # figure size fft_size = 512 # fft size for analysis fft_hop = 8 # hop between analysis frame fft_zp = 512 analysis_window = np.concatenate((windows.hann(fft_size), np.zeros(fft_zp))) t_cut = 0.83 # length in [s] to remove at end of signal (no sound) # 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 SNR_at_mic = 20 # SNR at center of microphone array in dB # Room 1 : Shoe box room_dim = [4, 6] # the good source is fixed for all good_source = [1, 4.5] # good source normal_interferer = [2.8, 4.3] # interferer
import Room as rg
import beamforming as bf
from constants import eps
from stft import stft, spectroplot
import windows
import utilities as u
# Spectrogram figure properties
figsize = (7.87, 1.65) # figure size
figsize2 = (7.87, 1.5 * 1.65) # figure size
fft_size = 512 # fft size for analysis
fft_hop = 8 # hop between analysis frame
fft_zp = 512
analysis_window = np.concatenate((windows.hann(fft_size), np.zeros(fft_zp)))
t_cut = 0.83 # length in [s] to remove at end of signal (no sound)
# 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
SNR_at_mic = 20 # SNR at center of microphone array in dB
# Room 1 : Shoe box
room_dim = [4, 6]
# the good source is fixed for all
good_source = [1, 4.5] # good source
