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added condensation for frequencies + adjustments top frequencies and amp collection

This commit is contained in:
Alexandre 2024-11-04 17:49:32 +01:00
parent cba14e4782
commit c969501c52
6 changed files with 140 additions and 516 deletions
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503
Linked Horizon - Shinzou o Sasageyo! [TV Size] (Monstrata) [Insane].osu
View File

@ -1,503 +0,0 @@
osu file format v14
[General]
AudioFilename: audio.wav
AudioLeadIn: 0
PreviewTime: 63278
Countdown: 0
SampleSet: Normal
StackLeniency: 0.5
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LetterboxInBreaks: 0
WidescreenStoryboard: 1
[Editor]
DistanceSpacing: 1.2
BeatDivisor: 4
GridSize: 32
TimelineZoom: 1.8
[Metadata]
Title:Shinzou o Sasageyo! [TV Size]
TitleUnicode:心臓を捧げよ! [TV Size]
Artist:Linked Horizon
ArtistUnicode:Linked Horizon
Creator:Monstrata
Version:Insane
Source:進撃の巨人
Tags:revo Attack on Titan shingeki no kyojin season 2 two dedicate all your hearts heart snk aot Haruto haruto_aizawa armin arlert levi erin jaeger mikasa ackerman survey corps opening one theme song sound
BeatmapID:1256136
BeatmapSetID:593620
[Difficulty]
HPDrainRate:5.5
CircleSize:4
OverallDifficulty:7.5
ApproachRate:9
SliderMultiplier:2.2
SliderTickRate:1
[Events]
//Background and Video events
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//Break Periods
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//Storyboard Layer 1 (Fail)
//Storyboard Layer 2 (Pass)
//Storyboard Layer 3 (Foreground)
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263,226,45560,6,0,P|232:216|200:216,1,65.9999979858399,4|0,0:1|0:0,0:0:0:0:
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237,206,47622,1,2,0:0:0:0:
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289,58,48185,6,0,L|298:150,1,88,2|0,0:1|0:0,0:0:0:0:
435,71,48560,6,0,P|365:32|289:57,1,165,12|0,0:2|0:0,0:0:0:0:
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165,75,49310,1,10,0:0:0:0:
120,327,49497,1,0,0:0:0:0:
360,239,49685,1,8,0:0:0:0:
99,214,49872,1,0,0:0:0:0:
251,0,50060,6,0,L|246:103,1,88,12|0,1:2|0:0,0:0:0:0:
329,196,50341,1,0,0:0:0:0:
329,196,50435,2,0,L|333:108,1,88,8|0,1:3|0:0,0:0:0:0:
417,161,50716,1,0,0:0:0:0:
417,161,50810,6,0,L|488:150,1,44,4|0,1:2|1:0,0:0:0:0:
451,47,50997,2,0,L|407:53,1,44,8|0,1:3|1:0,0:0:0:0:
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412,171,52122,1,2,0:0:0:0:
267,185,52310,2,0,P|262:247|298:292,1,110,8|2,0:0|0:1,0:0:0:0:
343,220,52872,1,2,0:0:0:0:
488,136,53060,6,0,P|492:198|456:243,1,110,2|0,0:2|0:0,0:0:0:0:
412,171,53622,1,2,0:0:0:0:
259,79,53810,2,0,P|254:141|290:186,1,110,12|2,0:0|0:1,0:0:0:0:
343,122,54372,1,2,0:0:0:0:
343,122,54466,1,8,0:0:0:0:
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389,194,55122,1,2,0:0:0:0:
169,122,55310,2,0,P|122:109|54:144,1,110,8|2,0:0|0:1,0:0:0:0:
123,194,55872,1,2,0:0:0:0:
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423,206,58122,1,2,0:2:0:0:
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141,362,65435,2,0,L|82:364,1,58.4375011146069,0|0,0:0|3:0,0:0:0:0:
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227,70,66278,1,0,3:0:0:0:
184,74,66372,1,8,0:3:0:0:
151,102,66466,1,0,0:0:0:0:
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294,228,66841,1,0,0:0:0:0:
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387,5,68341,1,0,0:0:0:0:
387,5,68435,2,0,P|366:26|356:53,1,58.4375011146069,0|0,3:0|3:0,0:0:0:0:
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354,303,69091,1,0,0:0:0:0:
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289,216,71341,1,0,0:0:0:0:
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247,345,72278,1,10,0:3:0:0:
291,343,72372,1,12,0:3:0:0:
325,318,72466,1,8,0:2:0:0:
340,277,72560,5,14,0:2:0:0:
340,277,73310,5,2,0:2:0:0:
283,212,73497,1,2,3:0:0:0:
294,187,73590,1,2,3:1:0:0:
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220,151,73965,1,2,1:0:0:0:
208,175,74059,5,4,0:1:0:0:
340,277,74247,2,0,L|440:261,1,82.5000031471254,2|2,0:2|0:0,0:0:0:0:
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425,15,75372,1,8,0:0:0:0:
273,149,75560,5,4,0:2:0:0:
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cleaned_sp.py
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@ -71,7 +71,7 @@ def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
print("Retrieving freqs from", offset, "to", songlen+offset, "...") print("Retrieving freqs from", offset, "to", songlen+offset, "...")
print("amplitudes are from", minfreq, "to", maxfreq) print("amplitudes are from", minfreq, "to", maxfreq)
while(current_time < songlen-segsize): while(current_time < songlen+offset-segsize):
# index corresponding to left boundary # index corresponding to left boundary
left_id = int(current_time*sample_rate) left_id = int(current_time*sample_rate)
@ -136,8 +136,8 @@ def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampth
else: else:
song_data = raw_song_data song_data = raw_song_data
# remove the copy of the song print("\nSampleRate : ", sample_rate)
subprocess.run(["rm", "crop.wav"], shell=False) print("SegSize : ", blit)
# calculate the frequencies associated to the FFTs # calculate the frequencies associated to the FFTs
pfreq = scp.fft.rfftfreq(blit, 1/sample_rate) pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
@ -152,8 +152,8 @@ def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampth
k = 0 k = 0
print("Retrieving freqs from", offset, "to", songlen+offset, "...") print("Retrieving freqs from", offset, "to", songlen+offset, "...")
print("amplitudes are from", minfreq, "to", maxfreq) print("Freqs are from", minfreq, "to", maxfreq)
while(current_time < songlen-segsize): while(current_time < songlen+offset-segsize):
# index corresponding to left boundary # index corresponding to left boundary
left_id = int(current_time*sample_rate) left_id = int(current_time*sample_rate)
@ -172,15 +172,18 @@ def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampth
print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n") print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
# remove # remove
# i = time, j = freq
for i in range(len(fft_list)): for i in range(len(fft_list)):
# get the local max freq # get the local max freq
lmax = 0 lmax = 0
for j in range(len(fft_list[i])): for j in range(len(fft_list[i])):
if(np.abs(fft_list[i][j]) > lmax): if(np.abs(fft_list[i][j]) > lmax):
lmax = np.abs(fft_list[i][j]) lmax = np.abs(fft_list[i][j])
# remove freqs + amps # remove freqs + amps
for j in range(len(fft_list[i])): for j in range(len(fft_list[i])):
if(j < len(pfreq)):
if(pfreq[j] <= minfreq or pfreq[j] >= maxfreq): if(pfreq[j] <= minfreq or pfreq[j] >= maxfreq):
fft_list[i][j] = 0+0j fft_list[i][j] = 0+0j
@ -206,6 +209,121 @@ def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampth
res = np.array(res) res = np.array(res)
wavfile.write(output_name, sample_rate, res) wavfile.write(output_name, sample_rate, res)
def is_within_reach(xref, x, error):
return(np.abs(xref - x) <= error)
def localize_frequencies(song_name, offset, songlen, segsize, output_name):
# removes unnecessary frequencies/amps from a song
# ampthr is in [0, 1]
# extracting data from cropped song
sample_rate, raw_song_data = wavfile.read(song_name)
blit = int(sample_rate*segsize) # Te
song_data = [0 for i in range(len(raw_song_data))]
a = 0
if(is_data_stereo(raw_song_data)):
print("Converting to mono...")
for x in range(len(raw_song_data)):
song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
if(x % (int(len(raw_song_data)/100)) == 0):
print(a, "/ 100")
a += 1
else:
song_data = raw_song_data
print("\nSampleRate : ", sample_rate)
print("SegSize : ", blit)
# calculate the frequencies associated to the FFTs
pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
# left boundary of segment to crop
current_time = offset
# list of FFTs
fft_list = []
# number of samples
k = 0
print("Retrieving freqs from", offset, "to", songlen+offset, "...")
while(current_time < songlen+offset-segsize):
# index corresponding to left boundary
left_id = int(current_time*sample_rate)
# index corresponding to right boundary
right_id = int((current_time+segsize)*sample_rate)
# calculate the fft, append it to fft_list
pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
fft_list.append(pff)
# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
k += 1
current_time = offset + k*segsize
#print(current_time)
print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
let_ring_currentLength = 0
let_ring_freq = -200
let_ring_freqLeniency = 37
let_ring_leniencySecond = 0.07
let_ring_lengthLeniency = 2
# remove
# i = time, j = freq
for i in range(len(fft_list)):
# retrieve dominant freq
maxfreq = 0
maxfreqid = 0
maxamp = 0
for j in range(len(fft_list[i])):
if(np.abs(fft_list[i][j]) > maxamp):
maxamp = np.abs(fft_list[i][j])
maxfreq = pfreq[j]
maxfreqid = j
if(is_within_reach(maxfreq, let_ring_freq, let_ring_freqLeniency)):
let_ring_freq = maxfreq
let_ring_currentLength += 1
if(let_ring_currentLength > let_ring_lengthLeniency):
for j in range(len(fft_list[i])):
#if(j < len(pfreq) and is_within_reach(pfreq[j], maxfreq, 75)):
fft_list[i][j] = 0+0j
print("here")
else:
let_ring_freq = maxfreq
let_ring_currentLength = 0
print("(at time", i, ") maxfreq and length are", let_ring_freq, let_ring_currentLength)
# writing new .wav
res = []
print("Converting...")
for i in range(len(fft_list)):
ift = scp.fft.irfft(fft_list[i], n=blit)
for k in ift:
res.append(k)
#print(type(res[0]))
mx = 0
for j in range(len(res)):
if(res[j] > mx):
mx = res[j]
for i in range(len(res)):
res[i] = np.int16(32767*res[i]/mx)
res = np.array(res)
wavfile.write(output_name, sample_rate, res)
def retrieve_dominant_amps(song_name, offset, songlen, segsize, percent, divlen): def retrieve_dominant_amps(song_name, offset, songlen, segsize, percent, divlen):
# returns a list with the percent% peak amplitudes alongside the sample rate # returns a list with the percent% peak amplitudes alongside the sample rate
# /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono) # /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono)
@ -267,7 +385,7 @@ def retrieve_dominant_amps(song_name, offset, songlen, segsize, percent, divlen)
k = 0 k = 0
current_time = offset current_time = offset
while(current_time < songlen-segsize): while(current_time < songlen+offset-segsize):
# index corresponding to left boundary # index corresponding to left boundary
left_id = int(current_time*sample_rate) left_id = int(current_time*sample_rate)
@ -315,13 +433,15 @@ def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold
new_fn = convert_to_wav(filename) new_fn = convert_to_wav(filename)
print("Filtering song...") print("Filtering song...")
#void_freq_clean(new_fn, t0, t1-t0, dt, 200, 2500, 0.05, "crop1.wav") #void_freq_clean(new_fn, t0, t1, dtf, 20, 20000, 0.05, "crop1.wav")
#def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampthr, output_name):
print("Now retrieving the frequencies") print("Now retrieving the frequencies")
(maxlist, maxamps) = retrieve_dominant_freqs(new_fn, t0, t1, dtf) (maxlist, maxamps) = retrieve_dominant_freqs(new_fn, t0, t1, dtf)
#def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
print("Now retrieving the amplitudes") print("Now retrieving the amplitudes")
amps = retrieve_dominant_amps(new_fn, t0, t1, dta, threshold, 4/(bpm/60)) amps = retrieve_dominant_amps(new_fn, t0, t1, dta, threshold, (4/(bpm/60))/4)
print("Len of freqs : ", len(maxlist), "|", len(maxamps)) print("Len of freqs : ", len(maxlist), "|", len(maxamps))
print("Len of amps : ", len(maxlist), "|", len(amps)) print("Len of amps : ", len(maxlist), "|", len(amps))
@ -344,7 +464,14 @@ def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold
# free() # free()
retrieve_all_from_song("ctype.mp3", 0, 5, 149.3, dtf=1/(149.3/60)/8) '''
void_freq_clean(convert_to_wav("ctype.mp3"), 0.042, 5, 1/(149.3/60)/8, 100, 3000, 0.05, "ctype_void.mp3")
localize_frequencies(convert_to_wav("ctype_void.mp3"), 0, 5, 1/(149.3/60)/12, "ctype_filtered.mp3")
retrieve_all_from_song("ctype_filtered.mp3", 0, 5, 149.3, dta=1/(149.3/60)/128, dtf=1/(149.3/60)/8)
'''
void_freq_clean(convert_to_wav("tetris_2.wav"), 0.042, 5, 1/(149.3/60)/8, 100, 3000, 0.05, "tetris_2_void.wav")
localize_frequencies(convert_to_wav("tetris_2_void.wav"), 0, 5, 1/(149.3/60)/12, "tetris_2_f.wav")
retrieve_all_from_song("tetris_2_f.wav", 0, 5, 149.3, dtf=1/(149.3/60)/8)
print("yipee") print("yipee")

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