out_dir=out_dir)
# Pink Noise
songname = 'pnoise'
fs = 44100 # Hz
t = 6 * 60 # s
lpf_order = 4
f_cutoff = 300 # Hz
np.random.seed(1024)
b, a = butter(lpf_order, Wn=f_cutoff, btype='lowpass', fs=fs)
pn = util.pink_noise(int(t * fs))
pn = pn - np.mean(pn)
pn = lfilter(b, a, pn)
pn = pn - np.mean(pn)
pn = pn / np.max(np.abs(pn)) * 0.25 # peak -12 dB
hpre = util.constant_phase_shifter(filter_order, np.deg2rad(283), beta=8.6)[1]
pn = util.acausal_filter(pn, hpre) # pre-shifting for decreasing crest factor
x = pn
n_start, n_stop = 9747840, 9851538
t_fadein, t_fadeout, t_intro, t_outro = 10, 10, 10, 10 # in milliseconds
util.make_aperiodic_stimuli(x,
fs,
songname,
n_start,
n_stop,
phase_angles,
filter_order,
t_fadein,
t_fadeout,
t_intro,
t_outro,
Y_dft = np.zeros((len(phase_angles), N // 2 + 1), dtype='complex128')
for i, phi in enumerate(phase_angles):
y = util.constant_phase_shift_dft(x_1p, phi)
y = util.periodic_signal(y, repetition)
y_dft[i] = y
Y_dft[i] = np.fft.rfft(y)
# Phase shift using FIR filter
long_repetition = 601 # very long burst train
n_start = util.t2n(300 * t_period, fs=fs, ms=True)
n_stop = util.t2n(303 * t_period, fs=fs, ms=True)
x_Mp = util.periodic_signal(x_1p, long_repetition)
y_fir = np.zeros((len(phase_angles), N)) # selection of long signal
Y_fir = np.zeros((len(phase_angles), N // 2 + 1), dtype='complex128')
for i, phi in enumerate(phase_angles):
h = util.constant_phase_shifter(filter_order, phi, beta=8.6)[1]
y = util.acausal_filter(x_Mp, h)[n_start:n_stop]
y_fir[i] = y
Y_fir[i] = np.fft.rfft(y)
# I. Float64
# Waveforms - DFT vs FIR methods
t = np.arange(len(y_fir[0])) / fs * 1000
fig, ax = plt.subplots(figsize=(12, 5), ncols=3, sharey=True)
for i, (phi, yd, yf) in enumerate(zip(phase_angles, y_dft, y_fir)):
ax[i].plot(t, util.db(yd), c='lightgray', label='DFT')
ax[i].plot(t, util.db(yd - yf), label='diff')
ax[i].set_xlabel('$t$ / ms')
ax[i].set_title('{:0.0f}'.format(np.rad2deg(phi)))
ax[i].grid(True)
filename = 'castanets.wav' start, stop = 6650, 6850 s0, fs = sf.read(dir_src + filename, start=start, stop=stop) # Upsampling oversample = 2 s0 = resample(s0, num=len(s0) * oversample) fs *= oversample # Phase angles and shifter order phimin, phimax, phinum = 0, 2 * np.pi, 8 phase_angles = np.linspace(phimin, phimax, num=phinum, endpoint=False) filter_order = 2**15 # Hilbert transform hH = util.constant_phase_shifter(filter_order, -0.5 * np.pi, beta=8.6)[1] sH = util.acausal_filter(s0, hH) envelope = np.sqrt(s0**2 + sH**2) # Constant phase shift signals = [np.cos(phi) * s0 - np.sin(phi) * sH for phi in phase_angles] # time axis time = util.n2t(start + np.arange(len(s0)), fs=fs, ms=True) # Plots ext = '.pdf' # Original fig, ax = plt.subplots(figsize=(12, 6))
# Plots
colors = cm.get_cmap('viridis', len(phase_angles)).colors
phi_ticks = np.arange(0, 180 + 45, 45)
phi_lim = -10, 190
fontsize_label = 9
f_maglabel = 8000
f_phaselabel = 8000
voffset_tf = 6
voffset_maglabel = 0.5
voffset_phaselabel = 1.875
fig, ax = plt.subplots(ncols=2, gridspec_kw={'wspace': 0.25})
for i, fo in enumerate(Filter_orders):
_, h = util.constant_phase_shifter(fo, phase_varN, beta=beta)
_, H = freqz(h, 1, f, fs=fs)
gd = fo / 2 / fs
H *= np.exp(1j * omega * gd) # compensate group delay
phase = np.unwrap(np.mod(np.angle(H), 2 * np.pi))
phase_deg = np.rad2deg(phase)
vshift = -idx_varN * voffset_tf
color = colors[idx_varN]
opacity = 1.2**(-i)
# Frequency responses for varying filter order
ax[0].semilogx(f,
vshift + db(H),
color=color,
lw=1.5,