From f37247f8b706f1855c9410817980a76437e7de86 Mon Sep 17 00:00:00 2001
From: Sting <loic.allegre@ensiie.fr>
Date: Mon, 17 Jul 2023 15:02:51 +0200
Subject: [PATCH] Add magnitude histogram in test mode
---
cnn_madmom/segment.py | 27 +++++++++++++++++----------
1 file changed, 17 insertions(+), 10 deletions(-)
diff --git a/cnn_madmom/segment.py b/cnn_madmom/segment.py
index 5e78190..b1455c7 100644
--- a/cnn_madmom/segment.py
+++ b/cnn_madmom/segment.py
@@ -160,8 +160,8 @@ if __name__ == "__main__":
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=0.25, smooth=0)
- onsets = np.array([o for o in onsets if cnn_smoothed[o] > 0.2])
+ onsets = madmom.features.onsets.peak_picking(activation_smoothed, threshold=0.1, smooth=0)
+ onsets = np.array([o for o in onsets if cnn_smoothed[o] > 0.1])
invalid_onsets_idx = []
magnitude_window = [2,8]
@@ -172,14 +172,18 @@ if __name__ == "__main__":
onsets = np.delete(onsets, invalid_onsets_idx)
- filtered_onsets = []
- for line in reference_syls:
- syl_number = len(line) - 1
- line_onsets = [o for o in onsets if (o >= line[0][0] and o <= line[-1][0])]
- line_onsets.sort(reverse=True, key=(lambda x: activation_smoothed[x]))
- filtered_onsets += line_onsets[0:syl_number]
-
- onsets = np.array(sorted(filtered_onsets))
+
+ if reference_syls:
+ filtered_onsets = []
+ for line in reference_syls:
+ syl_number = len(line) - 1
+ line_onsets = [o for o in onsets if (o >= line[0][0] and o <= line[-1][0])]
+ line_onsets.sort(reverse=True, key=(lambda x: activation_smoothed[x]))
+ if syl_number > len(line_onsets):
+ print("WARNING : failed to detect enough onsets in line")
+ filtered_onsets += line_onsets[0:syl_number]
+
+ onsets = np.array(sorted(filtered_onsets))
# Backtrack onsets to closest earlier local minimum
if backtrack:
@@ -203,5 +207,8 @@ if __name__ == "__main__":
axs[1].vlines(onsets, 0, 2, colors='red')
axs[1].hlines([max(magnitude_threshold, 0.5)], 0, onsets[-1]+100, colors='black')
+ bins = np.arange(0, 2, 0.05)
+ hist, hist_axs = plt.subplots(nrows=1)
+ hist_axs.hist(magnitude, bins=bins)
plt.show()
--
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