def illustrate_match(self, analyzer, ht, filename):
""" Show the query fingerprints and the matching ones
plotted over a spectrogram """
# Make the spectrogram
# d, sr = librosa.load(filename, sr=analyzer.target_sr)
d, sr = audio_read.audio_read(filename,
sr=analyzer.target_sr,
channels=1)
sgram = np.abs(
stft.stft(d,
n_fft=analyzer.n_fft,
hop_length=analyzer.n_hop,
window=np.hanning(analyzer.n_fft + 2)[1:-1]))
sgram = 20.0 * np.log10(np.maximum(sgram, np.max(sgram) / 1e6))
sgram = sgram - np.mean(sgram)
# High-pass filter onset emphasis
# [:-1,] discards top bin (nyquist) of sgram so bins fit in 8 bits
# spectrogram enhancement
if self.illustrate_hpf:
HPF_POLE = 0.98
sgram = np.array([
scipy.signal.lfilter([1, -1], [1, -HPF_POLE], s_row)
for s_row in sgram
])[:-1, ]
sgram = sgram - np.max(sgram)
librosa.display.specshow(sgram,
sr=sr,
hop_length=analyzer.n_hop,
y_axis='linear',
x_axis='time',
cmap='gray_r',
vmin=-80.0,
vmax=0)
# Do the match?
q_hashes = analyzer.wavfile2hashes(filename)
# Run query, get back the hashes for match zero
results, matchhashes = self.match_hashes(ht, q_hashes, hashesfor=0)
if self.sort_by_time:
results = sorted(results, key=lambda x: -x[2])
# Convert the hashes to landmarks
lms = audfprint_analyze.hashes2landmarks(q_hashes)
mlms = audfprint_analyze.hashes2landmarks(matchhashes)
# Overplot on the spectrogram
plt.plot(
np.array([[x[0], x[0] + x[3]] for x in lms]).T,
np.array([[x[1], x[2]] for x in lms]).T, '.-g')
plt.plot(
np.array([[x[0], x[0] + x[3]] for x in mlms]).T,
np.array([[x[1], x[2]] for x in mlms]).T, '.-r')
# Add title
plt.title("Matched as " +
ht.names[results[0][0]].split("/")[1].split(".")[0])
# Display
plt.savefig("./src/static/sgram" + uuid.uuid4().hex + ".png",
bbox_inces="tight")
# plt.show()
# Return
return results
def illustrate_match(self, analyzer, ht, filename):
""" Show the query fingerprints and the matching ones
plotted over a spectrogram """
# Make the spectrogram
#d, sr = librosa.load(filename, sr=analyzer.target_sr)
d, sr = audio_read.audio_read(filename, sr=analyzer.target_sr, channels=1)
sgram = np.abs(librosa.stft(d, n_fft=analyzer.n_fft,
hop_length=analyzer.n_hop,
window=np.hanning(analyzer.n_fft+2)[1:-1]))
sgram = 20.0*np.log10(np.maximum(sgram, np.max(sgram)/1e6))
sgram = sgram - np.mean(sgram)
# High-pass filter onset emphasis
# [:-1,] discards top bin (nyquist) of sgram so bins fit in 8 bits
# spectrogram enhancement
if self.illustrate_hpf:
HPF_POLE = 0.98
sgram = np.array([scipy.signal.lfilter([1, -1],
[1, -HPF_POLE], s_row)
for s_row in sgram])[:-1,]
sgram = sgram - np.max(sgram)
librosa.display.specshow(sgram, sr=sr, hop_length=analyzer.n_hop,
y_axis='linear', x_axis='time',
cmap='gray_r', vmin=-80.0, vmax=0)
# Do the match?
q_hashes = analyzer.wavfile2hashes(filename)
# Run query, get back the hashes for match zero
results, matchhashes = self.match_hashes(ht, q_hashes, hashesfor=0)
if self.sort_by_time:
results = sorted(results, key=lambda x: -x[2])
# Convert the hashes to landmarks
lms = audfprint_analyze.hashes2landmarks(q_hashes)
mlms = audfprint_analyze.hashes2landmarks(matchhashes)
# Overplot on the spectrogram
plt.plot(np.array([[x[0], x[0]+x[3]] for x in lms]).T,
np.array([[x[1], x[2]] for x in lms]).T,
'.-g')
plt.plot(np.array([[x[0], x[0]+x[3]] for x in mlms]).T,
np.array([[x[1], x[2]] for x in mlms]).T,
'.-r')
# Add title
plt.title(filename + " : Matched as " + ht.names[results[0][0]]
+ (" with %d of %d hashes" % (len(matchhashes),
len(q_hashes))))
# Display
plt.show()
# Return
return results
