From 399ab88fdc244c444e4a0d631ef7200bbcebb8b0 Mon Sep 17 00:00:00 2001
From: Sting <loic.allegre@ensiie.fr>
Date: Thu, 13 Jul 2023 13:56:17 +0200
Subject: [PATCH] Try with modified Kullback-Leibler
---
cnn_madmom/segment.py | 45 ++++++++++++++++++++++++-------------------
1 file changed, 25 insertions(+), 20 deletions(-)
diff --git a/cnn_madmom/segment.py b/cnn_madmom/segment.py
index 40034d3..a86921d 100644
--- a/cnn_madmom/segment.py
+++ b/cnn_madmom/segment.py
@@ -7,7 +7,8 @@ from scipy.ndimage.filters import maximum_filter
def segment(songfile):
- delay = 0
+ delay = -4
+ backtrack = False
cnn = madmom.features.onsets.CNNOnsetProcessor()
spectral = madmom.features.onsets.SpectralOnsetProcessor('complex_domain')
@@ -21,23 +22,24 @@ def segment(songfile):
spectral_function = spectral(songfile, num_channels=1)
spectral_function = spectral_function/(spectral_function.max())
- activation_function = 0.5*cnn_function + 0.5*spectral_function
- #activation_function = (2 * cnn_function * spectral_function)/(cnn_function + spectral_function)
+ #activation_function = 0.5*cnn_function + 0.5*spectral_function
+ activation_function = (2 * cnn_function * spectral_function)/(cnn_function + spectral_function)
#activation_function = np.where(spectral_function > 0.14, cnn_function, 0)
#onsets = proc(activation_function)
activation_smoothed = madmom.audio.signal.smooth(activation_function, 20)
cnn_smoothed = madmom.audio.signal.smooth(cnn_function, 20)
- onsets = madmom.features.onsets.peak_picking(activation_smoothed, threshold=1, smooth=0)
+ onsets = madmom.features.onsets.peak_picking(activation_smoothed, threshold=0.6, smooth=0)
onsets = np.array([o for o in onsets if cnn_smoothed[o] > 0.2])
- # Backtrack onsets to closest earlier local minimum
- backtrack_max_frames = 50
- for i in range(len(onsets)):
- initial_onset = onsets[i]
- while(activation_smoothed[onsets[i] - 1] < activation_smoothed[onsets[i]] and onsets[i] > initial_onset - backtrack_max_frames):
- onsets[i] -= 1
+ if backtrack:
+ # Backtrack onsets to closest earlier local minimum
+ backtrack_max_frames = 50
+ for i in range(len(onsets)):
+ initial_onset = onsets[i]
+ while(activation_smoothed[onsets[i] - 1] < activation_smoothed[onsets[i]] and onsets[i] > initial_onset - backtrack_max_frames):
+ onsets[i] -= 1
onsets = (onsets + delay)/100
@@ -50,8 +52,10 @@ def segment(songfile):
if __name__ == "__main__":
songfile = sys.argv[1]
+ backtrack = False
+
cnn = madmom.features.onsets.CNNOnsetProcessor()
- spectral = madmom.features.onsets.SpectralOnsetProcessor('complex_domain')
+ spectral = madmom.features.onsets.SpectralOnsetProcessor('modified_kullback_leibler')
spec = spec = madmom.audio.spectrogram.Spectrogram(songfile, num_channels=1)
@@ -62,22 +66,23 @@ if __name__ == "__main__":
spectral_function = spectral(songfile, num_channels=1)
spectral_function = spectral_function/(spectral_function.max())
- activation_function = 0.5*cnn_function + 0.5*spectral_function
- #activation_function = (2 * cnn_function * spectral_function)/(cnn_function + spectral_function)
+ #activation_function = 0.5*cnn_function + 0.5*spectral_function
+ activation_function = (2 * cnn_function * spectral_function)/(cnn_function + spectral_function)
#activation_function = np.where(spectral_function > 0.14, cnn_function, 0)
#onsets = proc(activation_function)
activation_smoothed = madmom.audio.signal.smooth(activation_function, 20)
cnn_smoothed = madmom.audio.signal.smooth(cnn_function, 20)
- onsets = madmom.features.onsets.peak_picking(activation_smoothed, threshold=1, smooth=0)
+ onsets = madmom.features.onsets.peak_picking(activation_smoothed, threshold=0.6, smooth=0)
onsets = np.array([o for o in onsets if cnn_smoothed[o] > 0.2])
# Backtrack onsets to closest earlier local minimum
- backtrack_max_frames = 50
- for i in range(len(onsets)):
- initial_onset = onsets[i]
- while(activation_smoothed[onsets[i] - 1] < activation_smoothed[onsets[i]] and onsets[i] > initial_onset - backtrack_max_frames):
- onsets[i] -= 1
+ if backtrack:
+ backtrack_max_frames = 50
+ for i in range(len(onsets)):
+ initial_onset = onsets[i]
+ while(activation_smoothed[onsets[i] - 1] < activation_smoothed[onsets[i]] and onsets[i] > initial_onset - backtrack_max_frames):
+ onsets[i] -= 1
print(onsets/100)
@@ -85,7 +90,7 @@ if __name__ == "__main__":
axs[0].imshow(log_spec.T, origin='lower', aspect='auto')
axs[1].plot(cnn_smoothed)
axs[1].plot(spectral_function, color='green')
- axs[1].plot(activation_smoothed, color='pink')
+ axs[1].plot(activation_smoothed, color='orange')
axs[1].vlines(onsets, 0, 1, colors='red')
plt.show()
--
GitLab