def go(impulses_csv=IMPULSES_CSV, make_plots=False):
'''
Initializes an ImpulseResponses object, processes impulses and exports them
to disk. Plots are generated if specified and saved to disk.
'''
impulses = ImpulseResponses(impulses_csv)
impulses.export_to_npz()
# generate plots of the processed impulse response
if make_plots:
for freq_bin in FREQ_BINS:
freq_impulses = []
for impulse in impulses.impulses_ffts.keys():
freq_impulse = impulses.impulses_ffts[impulse][str(freq_bin)]
freq_impulses.append(freq_impulse)
# plot each impulse at given frequency separately
plot([freq_impulse], impulse + 'bin_{}'.format(freq_bin))
# plot all impulses at given frequency together
plot(freq_impulses, 'processed_impulses_bin_{}'.format(freq_bin))
def go(audio_fname, impulses_fname=PROCESSED_IMPULSES_CSV, k=K_NEIGHBORS, make_plots=False):
'''
Initializes a ProcessedImpulses object with the already processed impulses.
Initializes a ReverbAudio object using the audio_fname and extracts a reverb signature.
Calls k_neihbors module to perform a k-nearest neighbors analysis between the impulses
and the audio reverb signature.
Plots are generated if specified and saved to disk.
'''
impulses = ProcessedImpulses(impulses_fname)
reverb = ReverbAudio(audio_fname)
analysis = k_neighbors.KNeighbors(impulses.impulses, reverb.reverb_signature)
analysis_results = analysis.do_analysis(k=k, make_plots=make_plots)
# generate plots of the reverb signature at each frequency bin
if make_plots:
for freq_bin in FREQ_BINS:
reverb_signature = reverb.reverb_signature[str(freq_bin)]
if reverb_signature[0] != None:
plot([reverb_signature], reverb.audio.title + '_bin_' + str(freq_bin))
print('A lower value means a better match.')
print('A value of 0.0 for k=1 means the match is exact.')
print(analysis_results)
return analysis_results
