nperseg=100)
print(t.shape, f.shape, Zxx.shape)
# print(f.shape, f[:10])
# print(t.shape, t[:10])
# print(Zxx.shape, Zxx[:10])
# print(len([stft**2 for stft in Zxx]), [stft**2 for stft in Zxx][:10])
signal_fig = FigData(
xs=nst.samp_nums,
ys=[nst.data],
title=f"(a) Non stationary signal (samp rate = {samp_rate})",
plot_type="plot",
xlabel="Time (s)",
ylabel="Amplitude")
stft_fig = apreset.stft_pcolormesh(t=t,
f=f,
Zxx=Zxx,
title=f"(b) STFT Magnitude",
yscale="linear",
ylim=(0, 50),
colorbar_params={"pad": 0.05})
aplot.single_subplots(grid_size=(1, 2),
fig_data={
(0, 0): signal_fig,
(0, 1): stft_fig
},
individual_figsize=(5, 3.5),
savefig_path="stft_sines.png")
sine_fft_fig = FigData(xs=sine_fft_x[:sine_slice_num],
ys=np.abs(sine_fft[:sine_slice_num]),
title=sine_sum_fft_title,
options=["grid"],
**fft_axis_labels)
#
# Plot
#
aplot.single_subplots(grid_size=(3, 1),
fig_data={
(0, 0): cl_raw_fig,
(1, 0): cl_signal_fig,
(2, 0): cl_fft_fig
},
individual_figsize=(8, 2.8),
dpi=300,
savefig_path="Clarinet_FFT")
aplot.single_subplots(
grid_size=(3, 1),
fig_data={
(0, 0): sine_raw_fig,
(1, 0): sine_signal_fig,
(2, 0): sine_fft_fig
},
individual_figsize=(12, 3),
savefig_path=f"{str(sine_signal.sin_freqs).replace(' ', '')}Hz_Sine_Sum_FFT"
)
# Create figures
figs[(row, col)] = FigData(xs=np.array(range(Nx)),
ys=[window_data[window_name]],
title=window)
figs_response[(row, col)] = FigData(xs=np.array(freq),
ys=[response],
title=window,
xlim=(-0.5, 0.5),
ylim=(-120, 0))
#
# Plot window data
#
aplot.single_subplots(grid_size=(nrows, ncols),
fig_data=figs,
individual_figsize=(3.5, 1.6),
title="All Scipy Supported Windows",
xlabel="Sample",
ylabel="Amplitude",
savefig_path="scipy_windows")
aplot.single_subplots(grid_size=(nrows, ncols),
fig_data=figs_response,
individual_figsize=(3.5, 1.6),
title="Frequency Response of each window",
xlabel="Normalized frequency (cycles per sample)",
ylabel="Normalized magnitude (dB)",
savefig_path="scipy_windows_freq_responses")
recon_fig = FigData(xs=trec,
ys=xrec,
title="Reconstructed after filtering:\n"
f"{scale_name} minor triads w/ inversions",
plot_type="plot",
xlabel="Time (s)",
ylabel="Amplitude")
#
# Display results
#
aplot.single_subplots(
grid_size=(2, 2),
fig_data={
(0, 1): stft_fig,
(1, 1): stftf_fig,
(0, 0): signal_fig,
(1, 0): recon_fig
},
individual_figsize=(6, 4),
savefig_path=
f"ISTFT_{window_name}_{window_length}_{int(overlap_percent*100)}%_Pure_{scale_name}_minor_chord_sampr={samp_rate}"
)
wav.write(
f"audio/ISTFT_{window_name}_{window_length}_{int(overlap_percent * 100)}%_Pure_{scale_name}m",
np.concatenate((nst.data, xrec)),
samp_rate=samp_rate)
recon_stft_figs = [None for i in range(len(recon_stfts))]
for i, recon_stft in enumerate(recon_stfts):
recon_stft_figs[i] = apreset.stft_pcolormesh(
t=t,
f=f,
Zxx=recon_stft,
title=
f"Separated source '{sep_names[i]}'\nat d={ds[i]} with width H={Hs[i]}",
yscale="linear",
ylim=ylim)
fig_data[(2, i + 1)] = recon_stft_figs[i]
# Plot null/peaks, src & source STFTs
aplot.single_subplots(
grid_size=(3, 4),
fig_data=fig_data,
individual_figsize=(6, 4),
# savefig_path=f"ADRess_{channel}_{tech_name}.png"
)
#
# Output audio
#
wav.write(f"audio/{name}_stereo", src.T, samp_rate=samp_rate)
wav.write(f"audio/{name}_left", left_signal, samp_rate=samp_rate)
wav.write(f"audio/{name}_right", right_signal, samp_rate=samp_rate)
for i, left_recon in enumerate(left_recons):
wav.write(f"audio/{tech_name}_{short_sep_names[i]}",
left_recon,
samp_rate=samp_rate)
fft_figs = [FigData(xs=[np.linspace(0, max_response_freq, 1024), x[:psns[i]+1]],
ys=[zp_responses[i][:1024], responses[i][:psns[i]+1]],
title=f"({fig_labels[i * 2 + 1]}) Magnitude of DFT ({freq * signals[i].duration} cycles)",
options=["grid", "xres"],
line_options=fft_line_options,
**fft_axis_labels)
for i, x in enumerate(freqs)]
ws_fft_fig = FigData(xs=[np.linspace(0, max_response_freq, 1024), freqs[1][:psns[1]+1]],
ys=[wszp_response[:1024], ws_response[:psns[1]+1]],
title=f"(f) Magnitude of DFT (Windowed, {freq * signals[1].duration} cycles)",
options=["grid", "xres"],
line_options=fft_line_options,
**fft_axis_labels)
#
# Plot
#
aplot.single_subplots(grid_size=(3, 2),
fig_data={(0, 0): sig_figs[0],
(0, 1): fft_figs[0],
(1, 0): sig_figs[1],
(1, 1): fft_figs[1],
(2, 0): ws_fig,
(2, 1): ws_fft_fig},
individual_figsize=(5.5, 3),
title=f"{freq}Hz Sinusoid sampled at {samp_rate}Hz",
savefig_path="Spectral_Leakage"
)
ys=[sig.data],
title=f"({chr(label)}) {name} of {note_names}",
plot_type="plot",
xlabel="Time (s)",
ylabel="Amplitude")
label += 2
fft_figs[i] = FigData(xs=fft_x[:slice_num],
ys=np.abs(ft) ** 2,
title=f"({chr(label)}) Fourier Transform Magnitude of {name}",
options=["grid"],
xlabel="Frequency (Hz)",
ylabel="Normalised Magnitude")
label -= 1
# Plot
aplot.single_subplots(grid_size=(2, 2),
fig_data={(0, 0): sig_figs[0],
(0, 1): sig_figs[1],
(1, 0): fft_figs[0],
(1, 1): fft_figs[1]},
individual_figsize=(4, 2.5),
auto_timestamp=True,
savefig_path=f"{underline_note_names}_{round(duration*1000)}ms_{name}_FFT.png"
)
# Output wav
# wav.write(f"audio/{underline_note_names}_{round(duration*1000)}ms_melody", melody)
# wav.write(f"audio/{underline_note_names}_{round(duration*1000)}ms_chord", chord)
plot_type="plot",
xlabel="Time (s)",
ylabel="Amplitude")
fft_fig = FigData(xs=fft_x[:slice_num],
ys=np.abs(ws_ffts[seg])**2,
title="Fourier Transform Magnitude",
options=["grid"],
ylim=(-0.025, 0.25),
xlabel="Frequency (Hz)",
ylabel="Normalised Amplitude")
aplot.single_subplots(grid_size=(2, 1),
fig_data={(0, 0): ws_fig,
(1, 0): fft_fig},
individual_figsize=(5, 2.5),
auto_timestamp=False,
folder="stft_schematic",
savefig_path=f"tau={tau}s.png"
)
# Plot the 3D vis of the FFTs which forms the STFT
x, y = np.meshgrid(fft_x, np.arange(0, n_segments) / sum(durations))
fft_stack_fig = FigData(xs=x,
ys=y,
zs=[np.abs(np.array(ws_ffts))**2],
title="STFT surface from stacking FFT",
plot_type="plot_surface",
xlabel="Frequency (Hz)",
ylabel="Time (s)",
zlabel="Normalised Amplitude",
line_options=[{"cmap": "viridis"}],
r_stft_rec_fig = apreset.stft_pcolormesh(t=t,
f=f,
Zxx=right_stft_recon,
title=f"Separated source at d={right_d} with width H={right_H}",
yscale="linear",
ylim=r_ylim)
#
# Output
#
# Plot the frequency-azimuth spectrogram at the specified time frame
aplot.single_subplots(grid_size=(2, 2),
fig_data={(0, 0): l_azi_null_fig,
(1, 0): l_azi_peak_fig,
(0, 1): l_stft_src_fig,
(1, 1): l_stft_rec_fig,
},
individual_figsize=(6, 4),
savefig_path=f"ADRess_left_{tech_name}.png")
aplot.single_subplots(grid_size=(2, 2),
fig_data={(0, 0): r_azi_null_fig,
(1, 0): r_azi_peak_fig,
(0, 1): r_stft_src_fig,
(1, 1): r_stft_rec_fig,
},
individual_figsize=(6, 4),
savefig_path=f"ADRess_right_{tech_name}.png")
# Output audio file
for i, chord in enumerate(chord_names):
"label": window.__name__
}],
**signal_axis_labels)
cl_fft_fig = FigData(
xs=cl_fft_x[:cl_slice_num],
ys=[np.abs(cl_fft[:cl_slice_num]),
np.abs(cl_window_fft[:cl_slice_num])],
title=cl_fft_title,
line_options=[{
"label": cl_signal_title
}, {
"label": cl_window_title
}],
options=["grid"],
plot_type="semilogy",
**fft_axis_labels)
#
# Plot
#
aplot.single_subplots(grid_size=(2, 2),
fig_data={
(0, 0): cl_signal_fig,
(0, 1): cl_window_fig,
(1, 0, 1, 2): cl_fft_fig
},
individual_figsize=(6, 4),
savefig_path=f"Clarinet_FFT_Full_{window.__name__}")
f"STFT Magnitude (Zoomed) of {scale_name} Major scale\n(Artificial clarinet sound)",
line_options=[{
"vmin": 0,
"vmax": Zxx_max,
"shading": 'gouraud'
}],
options=["grid"],
plot_type="pcolormesh",
xlabel="Time (s)",
ylabel="Frequency (Hz)",
fit_data=False)
#
# Display results
#
aplot.single_plot(fig_data=signal_fig)
aplot.single_subplots(
grid_size=(2, 2),
fig_data={
(0, 0): fft_fig,
(1, 0): fft_zoomed_fig,
(0, 1): stft_fig,
(1, 1): stft_zoomed_fig
},
individual_figsize=(6, 4),
savefig_path=f"STFT_Clarinet_{scale_name}_Major_scale_sampr={samp_rate}")
# wav.write(f"audio/cl_{scale_name}_scale", nst)
window=window,
nperseg=nperseg,
noverlap=noverlap)
true_pos_stft_fig = apreset.stft_pcolormesh(t=t,
f=f,
Zxx=stft,
title=f"True signal at stereo position {pos}",
yscale="linear",
ylim=global_ylim,
colorbar_params={})
fig_data[(4, i)] = true_pos_stft_fig
# Plot L/R STFTs, null/peaks, recon STFTs & true STFTs at each stereo pos
aplot.single_subplots(grid_size=(5, 4),
fig_data=fig_data,
individual_figsize=(4.8, 3),
savefig_path=f"StereoADRess_{tech_name}.png",
show=False
)
aplot.single_subplots(grid_size=(4, 2),
fig_data={(0, 0): fig_data[(0, 0)],
(0, 1): fig_data[(0, 1)]},
individual_figsize=(4.8, 3),
savefig_path=f"ADRess_{tech_name}_LR.png",
show=False
)
aplot.single_subplots(grid_size=(4, 2),
fig_data={(0, 0): fig_data[(1, 0)],
(0, 1): fig_data[(2, 0)],
(1, 0): fig_data[(1, 1)],
nperseg=nperseg,
noverlap=noverlap)
true_pos_stft_fig = apreset.stft_pcolormesh(
t=t,
f=f,
Zxx=stft,
title=f"True signal at stereo position {pos}",
yscale="linear",
ylim=global_ylim,
max_mag=vmax)
fig_data[(4, i)] = true_pos_stft_fig
# Plot L/R STFTs, null/peaks, recon STFTs & true STFTs at each stereo pos
aplot.single_subplots(grid_size=(5, 4),
fig_data=fig_data,
individual_figsize=(6, 4),
savefig_path=f"FitzADRess_{tech_name}.png",
show=False)
#
# Output audio
#
wav.write(f"audio/{name}_stereo", src.T, samp_rate=samp_rate)
wav.write(f"audio/{name}_left", left_signal, samp_rate=samp_rate)
wav.write(f"audio/{name}_right", right_signal, samp_rate=samp_rate)
# True audio at each stereo position
for pos, audio in true_signals.items():
wav.write(f"audio/{name}_pos={pos}", audio, samp_rate=samp_rate)
null_fig = FigData(xs=np.arange(beta + 1),
ys=f,
zs=nulls[i],
title=f"Frequency-azimuth spectrogram\n(tau={'{:.2f}'.format(tau)}s)",
line_options=azi_line_options,
ylim=(0, 4000),
**azi_fig_params)
azi_line_options[0]["vmax"] = np.max(peaks[i]) * 0.25
peak_fig = FigData(xs=np.arange(beta + 1),
ys=f,
zs=peaks[i],
title=f"Null magnitude estimation (tau={'{:.2f}'.format(tau)}s)",
line_options=azi_line_options,
ylim=(0, 4000),
**azi_fig_params)
fig_data[(0, i)] = null_fig
fig_data[(1, i)] = peak_fig
# Plot null/peaks
aplot.single_subplots(grid_size=(2, len(taus)),
fig_data=fig_data,
individual_figsize=(6, 4),
folder=f"URMP_Analysis/{idx}",
savefig_path=f"{taus}.png".replace(" ", ""),
auto_timestamp=False,
show=False
)