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Audio_Process.py
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import madmom
import numpy as np
import os
import sys
from pathlib import Path
import musicsections
extensions = ["mp3", "wav", "ogg", "flac"] # we will look for all those file types.
def rhythmic_patterns_analysis(input_file, annotations_file, delimiter = ',',
downbeat_label = '.1', n_tatums = 4, n_clusters = 4):
'''Rhythmic patterns analysis
:parameters:
- input_file : str
path to input audio file (wav, mp3, m4a, flac, etc.)
- annotations_file : str
path to the annotations file (txt, csv, etc)
- delimiter: str
delimiter string to process the annotations file
- downbeat_label: str
string to look for in the label data to select downbeats
- n_tatums: int
number of tatums (subdivisions) per tactus beat
- n_clusters: int
number of clusters for rhythmic patterns clustering
'''
# =================== MAIN PROCESSING ===================
import carat
# 1. load the wav file
print('Loading audio file ...', input_file)
sr = 44100
y = madmom.audio.signal.Signal(input_file, sample_rate = sr,dtype = np.float32,num_channels=1)
# 2. load beat and downbeat annotations
print('Loading beat and downbeat annotations ...', annotations_file)
beats, _ = carat.annotations.load_beats(annotations_file, delimiter=delimiter)
downbeats, _ = carat.annotations.load_downbeats(annotations_file, delimiter=delimiter,
downbeat_label='1')
# number of beats per bar
n_beats = int(round(beats.size/downbeats.size))
# 3. compute accentuation feature
print('Computing accentuation feature ...')
acce, times, _ = carat.features.accentuation_feature(y, sr, minfreq=20, maxfreq=200)
# 4. compute feature map
print('Computing feature map ...')
map_acce, _, _, _ = carat.features.feature_map(acce, times, beats, downbeats,
n_beats=n_beats, n_tatums=n_tatums)
# 5. cluster rhythmic patterns
print('Clustering rhythmic patterns ...')
cluster_labs, centroids, _ = carat.clustering.rhythmic_patterns(map_acce, n_clusters=n_clusters)
track_path = str(input_file)
annotation_path = str(annotations_file)
sub = ""
if(annotation_path[-5:] == "2.csv"):
if(track_path[-9:] == "drums.mp3" or track_path[-9:] == "drums.wav"):
track_path = track_path [:-9]
sub = "drums_beats_c2.csv"
elif(track_path[-8:] == "bass.mp3" or track_path[-8:] == "bass.wav"):
track_path = track_path [:-8]
sub = "bass_beats_c2.csv"
elif(track_path[-9:] == "other.mp3" or track_path[-9:] == "other.wav"):
track_path = track_path [:-9]
sub = "other_beats_c2.csv"
elif(track_path[-10:] == "vocals.mp3" or track_path[-10:] == "vocals.wav"):
track_path = track_path [:-10]
sub = "vocals_beats_c2.csv"
print(track_path)
else:
if(track_path[-9:] == "drums.mp3" or track_path[-9:] == "drums.wav"):
track_path = track_path [:-9]
sub = "drums_beats_c.csv"
elif(track_path[-8:] == "bass.mp3" or track_path[-8:] == "bass.wav"):
track_path = track_path [:-8]
sub = "bass_beats_c.csv"
elif(track_path[-9:] == "other.mp3" or track_path[-9:] == "other.wav"):
track_path = track_path [:-9]
sub = "other_beats_c.csv"
elif(track_path[-10:] == "vocals.mp3" or track_path[-10:] == "vocals.wav"):
track_path = track_path [:-10]
sub = "vocals_beats_c.csv"
print(track_path)
np.savetxt(track_path+sub, cluster_labs, delimiter=",")
def Process():
import os
import sys
stems = sys.argv[1:]
for stem in stems:
process(stem)
def process(stem = ""):
import madmom
import numpy as np
from sklearn.cluster import KMeans, AgglomerativeClustering, FeatureAgglomeration
import os
import sys
stem = stem
from os import path
basepath = path.dirname(__file__)
track_path = basepath + "/separated/mdx_extra/" + stem + "/"
audio_path = basepath + "/" + stem
#in_path = basepath
#out_path = in_path + "/separated/"
def find_files(in_path):
out_2 = []
for file in Path(in_path).iterdir():
if file.suffix.lower().lstrip(".") in extensions:
out_2.append(file)
return out_2
files = [str(f) for f in find_files(track_path)]
deepsim_model_folder = basepath+"/models/deepsim/"
fewshot_model_folder = basepath+"/models/fewshot/"
model_deepsim = musicsections.load_deepsim_model(deepsim_model_folder)
model_fewshot = musicsections.load_fewshot_model(fewshot_model_folder)
signal_drums = madmom.audio.signal.Signal(files[1], sample_rate = 44100,dtype = np.float32,num_channels=1)
signal_bass = madmom.audio.signal.Signal(files[0], sample_rate = 44100,dtype = np.float32,num_channels=1)
signal_other = madmom.audio.signal.Signal(files[2], sample_rate = 44100,dtype = np.float32,num_channels=1)
signal_vocals = madmom.audio.signal.Signal(files[3], sample_rate = 44100,dtype = np.float32,num_channels=1)
fs_drums = madmom.audio.signal.FramedSignal(signal_drums, frame_size=2048, hop_size = 441)
fs_bass = madmom.audio.signal.FramedSignal(signal_bass, frame_size=2048, hop_size = 441)
fs_other = madmom.audio.signal.FramedSignal(signal_other, frame_size=2048, hop_size = 441)
fs_vocals = madmom.audio.signal.FramedSignal(signal_vocals, frame_size=2048, hop_size = 441)
spec_drums = madmom.audio.spectrogram.Spectrogram(fs_drums)
spec_bass = madmom.audio.spectrogram.Spectrogram(fs_bass)
spec_other = madmom.audio.spectrogram.Spectrogram(fs_other)
spec_vocals = madmom.audio.spectrogram.Spectrogram(fs_vocals)
log_filt_spec_drums = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_drums, num_bands=8)
log_filt_spec_bass = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_bass, num_bands=8)
log_filt_spec_other = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_other, num_bands=8)
log_filt_spec_vocals = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_vocals, num_bands=8)
superflux_drums = madmom.features.onsets.superflux(log_filt_spec_drums)
superflux_bass = madmom.features.onsets.superflux(log_filt_spec_bass)
superflux_other = madmom.features.onsets.superflux(log_filt_spec_other)
superflux_vocals = madmom.features.onsets.superflux(log_filt_spec_vocals)
superflux_drums_norm = superflux_drums/superflux_drums.max()
superflux_bass_norm = superflux_bass/superflux_bass.max()
superflux_other_norm = superflux_other/superflux_other.max()
superflux_vocals_norm = superflux_vocals/superflux_vocals.max()
np.savetxt(track_path+"superflux_drums_norm.csv", superflux_drums_norm, delimiter=",")
np.savetxt(track_path+"superflux_bass_norm.csv", superflux_bass_norm, delimiter=",")
np.savetxt(track_path+"superflux_other_norm.csv", superflux_other_norm, delimiter=",")
np.savetxt(track_path+"superflux_vocals_norm.csv", superflux_vocals_norm, delimiter=",")
proc = madmom.features.beats.RNNBeatProcessor(post_processor = None)
predictions_drums = proc(track_path+"drums.wav")
predictions_bass = proc(track_path+"bass.wav")
predictions_other = proc(track_path+"other.wav")
predictions_vocals = proc(track_path+"vocals.wav")
import scipy.stats
mm_proc = madmom.features.beats.MultiModelSelectionProcessor(num_ref_predictions = None)
drums_beats = mm_proc(predictions_drums)
bass_beats = mm_proc(predictions_bass)
other_beats = mm_proc(predictions_other)
vocals_beats = mm_proc(predictions_vocals)
when_beats_drums = madmom.features.beats.BeatTrackingProcessor(fps=200)(drums_beats)
drums_res = scipy.stats.linregress(np.arange(len(when_beats_drums)), when_beats_drums)
drums_first = drums_res.intercept
beat_step_drums = drums_res.slope
print(beat_step_drums, 60/beat_step_drums)
when_beats_bass = madmom.features.beats.BeatTrackingProcessor(fps=200)(bass_beats)
bass_res = scipy.stats.linregress(np.arange(len(when_beats_bass)), when_beats_bass)
bass_first = bass_res.intercept
beat_step_bass = bass_res.slope
print(beat_step_bass, 60/beat_step_bass)
when_beats_other = madmom.features.beats.BeatTrackingProcessor(fps=200)(other_beats)
other_res = scipy.stats.linregress(np.arange(len(when_beats_other)), when_beats_other)
other_first = other_res.intercept
beat_step_other = other_res.slope
print(beat_step_other, 60/beat_step_other)
when_beats_vocals = madmom.features.beats.BeatTrackingProcessor(fps=200)(vocals_beats)
vocals_res = scipy.stats.linregress(np.arange(len(when_beats_vocals)), when_beats_vocals)
vocals_first = vocals_res.intercept
beat_step_vocals = vocals_res.slope
print(beat_step_vocals, 60/beat_step_vocals)
drums_beats = np.array(np.where(drums_beats > 0.25))[0]/(drums_beats.shape[0]/(signal_drums.shape[0]/signal_drums.sample_rate))
bass_beats = np.array(np.where(bass_beats > 0.1))[0]/(bass_beats.shape[0]/(signal_bass.shape[0]/signal_bass.sample_rate))
other_beats = np.array(np.where(other_beats > 0.15))[0]/(other_beats.shape[0]/(signal_other.shape[0]/signal_other.sample_rate))
######!!!!!!!!!!Adjust 0.0 up for lyrics
vocals_beats = np.array(np.where(vocals_beats > 0.15))[0]/(vocals_beats.shape[0]/(signal_vocals.shape[0]/signal_vocals.sample_rate))
frac = 1.0/4.0
drums_beat_count = 1
for i in range(drums_beats.shape[0]-1):
if((drums_beats[i+1] - drums_beats[i]) > beat_step_drums*frac):
drums_beat_count += 1
drums_beats_adj = np.zeros((drums_beat_count, 2))
drums_beats_adj[0] = [drums_beats[0],1]
current_beat = 0
for i in range(drums_beats.shape[0]-1):
if((drums_beats[i+1] - drums_beats[i]) > beat_step_drums*frac):
current_beat += 1
drums_beats_adj[current_beat] = [drums_beats[i+1], 1]
labels_drums = (drums_beats_adj[:,1]).astype(str)
drums_beats_adj[:,1] = labels_drums
if(drums_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"drums_beats_2.csv", drums_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"drums_beats_2.csv", drums_beats_adj, delimiter=",")
bass_beat_count = 1
for i in range(bass_beats.shape[0]-1):
if((bass_beats[i+1] - bass_beats[i]) > beat_step_bass*frac):
bass_beat_count += 1
bass_beats_adj = np.zeros((bass_beat_count, 2))
bass_beats_adj[0] = [bass_beats[0],1]
current_beat = 0
for i in range(bass_beats.shape[0]-1):
if((bass_beats[i+1] - bass_beats[i]) > beat_step_bass*frac):
current_beat += 1
bass_beats_adj[current_beat] = [bass_beats[i+1], 1]
labels_bass = (bass_beats_adj[:,1]).astype(str)
bass_beats_adj[:,1] = labels_bass
if(bass_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"bass_beats_2.csv", bass_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"bass_beats_2.csv", bass_beats_adj, delimiter=",")
other_beat_count = 1
for i in range(other_beats.shape[0]-1):
if((other_beats[i+1] - other_beats[i]) > beat_step_other*frac):
other_beat_count += 1
other_beats_adj = np.zeros((other_beat_count, 2))
other_beats_adj[0] = [other_beats[0],1]
current_beat = 0
for i in range(other_beats.shape[0]-1):
if((other_beats[i+1] - other_beats[i]) > beat_step_other*frac):
current_beat += 1
other_beats_adj[current_beat] = [other_beats[i+1], 1]
labels_other = (other_beats_adj[:,1]).astype(str)
other_beats_adj[:,1] = labels_other
if(other_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"other_beats_2.csv", other_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"other_beats_2.csv", other_beats_adj, delimiter=",")
if(vocals_beats.size < 20):
drums_beats_temp = np.array(np.where(mm_proc(predictions_drums) > 0.5))[0]
bass_beats_temp = np.array(np.where(mm_proc(predictions_bass) > 0.1))[0]
other_beats_temp = np.array(np.where(mm_proc(predictions_other) > 0.1))[0]
vocals_beats_temp = np.sort(np.hstack((drums_beats_temp, bass_beats_temp, other_beats_temp)))
combined_max_ind = np.max((drums_beats_temp.max(),bass_beats_temp.max(),other_beats_temp.max()))
drums_beats_instr = drums_beats_temp/(drums_beats_temp.shape[0]/(signal_drums.shape[0]/signal_drums.sample_rate))
bass_beats_instr = bass_beats_temp/(bass_beats_temp.shape[0]/(signal_bass.shape[0]/signal_bass.sample_rate))
other_beats_instr = other_beats_temp/(other_beats_temp.shape[0]/(signal_other.shape[0]/signal_other.sample_rate))
######!!!!!!!!!!Adjust 0.0 up for lyrics
vocals_beats_instr = vocals_beats_temp/(combined_max_ind/(signal_vocals.shape[0]/signal_vocals.sample_rate))
vocals_beats = vocals_beats_instr
vocals_beat_count = 1
for i in range(vocals_beats.shape[0]-1):
if((vocals_beats[i+1] - vocals_beats[i]) > beat_step_vocals*frac):
vocals_beat_count += 1
vocals_beats_adj = np.zeros((vocals_beat_count, 2))
vocals_beats_adj[0] = [vocals_beats[0],1]
current_beat = 0
for i in range(vocals_beats.shape[0]-1):
if((vocals_beats[i+1] - vocals_beats[i]) > beat_step_vocals*frac):
current_beat += 1
vocals_beats_adj[current_beat] = [vocals_beats[i+1], 1]
labels_vocals = (vocals_beats_adj[:,1]).astype(str)
vocals_beats_adj[:,1] = labels_vocals
if(vocals_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"vocals_beats_2.csv", vocals_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"vocals_beats_2.csv", vocals_beats_adj, delimiter=",")
drums_beats = mm_proc(predictions_drums)
bass_beats = mm_proc(predictions_bass)
other_beats = mm_proc(predictions_other)
vocals_beats = mm_proc(predictions_vocals)
drums_beats = np.array(np.where(drums_beats > 0.25))[0]/(drums_beats.shape[0]/(signal_drums.shape[0]/signal_drums.sample_rate))
bass_beats = np.array(np.where(bass_beats > 0.1))[0]/(bass_beats.shape[0]/(signal_bass.shape[0]/signal_bass.sample_rate))
other_beats = np.array(np.where(other_beats > 0.15))[0]/(other_beats.shape[0]/(signal_other.shape[0]/signal_other.sample_rate))
######!!!!!!!!!!Adjust 0.0 up for lyrics
vocals_beats = np.array(np.where(vocals_beats > 0.15))[0]/(vocals_beats.shape[0]/(signal_vocals.shape[0]/signal_vocals.sample_rate))
frac = 1.0/16.0
drums_beat_count = 1
for i in range(drums_beats.shape[0]-1):
if((drums_beats[i+1] - drums_beats[i]) > beat_step_drums*frac):
drums_beat_count += 1
drums_beats_adj = np.zeros((drums_beat_count, 2))
drums_beats_adj[0] = [drums_beats[0],1]
current_beat = 0
for i in range(drums_beats.shape[0]-1):
if((drums_beats[i+1] - drums_beats[i]) > beat_step_drums*frac):
current_beat += 1
drums_beats_adj[current_beat] = [drums_beats[i+1], 1]
labels_drums = (drums_beats_adj[:,1]).astype(str)
drums_beats_adj[:,1] = labels_drums
if(drums_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"drums_beats.csv", drums_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"drums_beats.csv", drums_beats_adj, delimiter=",")
bass_beat_count = 1
for i in range(bass_beats.shape[0]-1):
if((bass_beats[i+1] - bass_beats[i]) > beat_step_bass*frac):
bass_beat_count += 1
bass_beats_adj = np.zeros((bass_beat_count, 2))
bass_beats_adj[0] = [bass_beats[0],1]
current_beat = 0
for i in range(bass_beats.shape[0]-1):
if((bass_beats[i+1] - bass_beats[i]) > beat_step_bass*frac):
current_beat += 1
bass_beats_adj[current_beat] = [bass_beats[i+1], 1]
labels_bass = (bass_beats_adj[:,1]).astype(str)
bass_beats_adj[:,1] = labels_bass
if(bass_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"bass_beats.csv", bass_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"bass_beats.csv", bass_beats_adj, delimiter=",")
other_beat_count = 1
for i in range(other_beats.shape[0]-1):
if((other_beats[i+1] - other_beats[i]) > beat_step_other*frac):
other_beat_count += 1
other_beats_adj = np.zeros((other_beat_count, 2))
other_beats_adj[0] = [other_beats[0],1]
current_beat = 0
for i in range(other_beats.shape[0]-1):
if((other_beats[i+1] - other_beats[i]) > beat_step_other*frac):
current_beat += 1
other_beats_adj[current_beat] = [other_beats[i+1], 1]
labels_other = (other_beats_adj[:,1]).astype(str)
other_beats_adj[:,1] = labels_other
if(other_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"other_beats.csv", other_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"other_beats.csv", other_beats_adj, delimiter=",")
if(vocals_beats.size < 20):
drums_beats_temp = np.array(np.where(mm_proc(predictions_drums) > 0.5))[0]
bass_beats_temp = np.array(np.where(mm_proc(predictions_bass) > 0.1))[0]
other_beats_temp = np.array(np.where(mm_proc(predictions_other) > 0.1))[0]
vocals_beats_temp = np.sort(np.hstack((drums_beats_temp, bass_beats_temp, other_beats_temp)))
combined_max_ind = np.max((drums_beats_temp.max(),bass_beats_temp.max(),other_beats_temp.max()))
drums_beats_instr = drums_beats_temp/(drums_beats_temp.shape[0]/(signal_drums.shape[0]/signal_drums.sample_rate))
bass_beats_instr = bass_beats_temp/(bass_beats_temp.shape[0]/(signal_bass.shape[0]/signal_bass.sample_rate))
other_beats_instr = other_beats_temp/(other_beats_temp.shape[0]/(signal_other.shape[0]/signal_other.sample_rate))
######!!!!!!!!!!Adjust 0.0 up for lyrics
vocals_beats_instr = vocals_beats_temp/(combined_max_ind/(signal_vocals.shape[0]/signal_vocals.sample_rate))
vocals_beats = vocals_beats_instr
#print(vocals_beats)
vocals_beat_count = 1
for i in range(vocals_beats.shape[0]-1):
if((vocals_beats[i+1] - vocals_beats[i]) > beat_step_vocals*frac):
vocals_beat_count += 1
vocals_beats_adj = np.zeros((vocals_beat_count, 2))
vocals_beats_adj[0] = [vocals_beats[0],1]
current_beat = 0
for i in range(vocals_beats.shape[0]-1):
if((vocals_beats[i+1] - vocals_beats[i]) > beat_step_vocals*frac):
current_beat += 1
vocals_beats_adj[current_beat] = [vocals_beats[i+1], 1]
labels_vocals = (vocals_beats_adj[:,1]).astype(str)
vocals_beats_adj[:,1] = labels_vocals
if(vocals_beats_adj[0,0] == 0.0):
np.savetxt(track_path+"vocals_beats.csv", vocals_beats_adj[1:], delimiter=",")
else:
np.savetxt(track_path+"vocals_beats.csv", vocals_beats_adj, delimiter=",")
input_file_drums = track_path + "drums.wav"
annotations_file_drums = track_path + "drums_beats.csv"
annotations_file_drums_2 = track_path + "drums_beats_2.csv"
input_file_bass = track_path + "bass.wav"
annotations_file_bass = track_path + "bass_beats.csv"
annotations_file_bass_2 = track_path + "bass_beats_2.csv"
input_file_other = track_path + "other.wav"
annotations_file_other = track_path + "other_beats.csv"
annotations_file_other_2 = track_path + "other_beats_2.csv"
input_file_vocals = track_path + "vocals.wav"
annotations_file_vocals = track_path + "vocals_beats.csv"
annotations_file_vocals_2 = track_path + "vocals_beats_2.csv"
rhythmic_patterns_analysis(input_file_drums, annotations_file_drums)
rhythmic_patterns_analysis(input_file_drums, annotations_file_drums_2)
rhythmic_patterns_analysis(input_file_bass, annotations_file_bass)
rhythmic_patterns_analysis(input_file_bass, annotations_file_bass_2)
rhythmic_patterns_analysis(input_file_other, annotations_file_other)
rhythmic_patterns_analysis(input_file_other, annotations_file_other_2)
rhythmic_patterns_analysis(input_file_vocals, annotations_file_vocals)
rhythmic_patterns_analysis(input_file_vocals, annotations_file_vocals_2)
drums_loudness = fs_drums.rms()
bass_loudness = fs_bass.rms()
other_loudness = fs_other.rms()
vocals_loudness = fs_vocals.rms()
combined_rms = (drums_loudness + bass_loudness + other_loudness + vocals_loudness)
drums_delta = np.sqrt(drums_loudness) + drums_loudness*np.abs(np.gradient(drums_loudness))
bass_delta = np.sqrt(bass_loudness) + bass_loudness*np.abs(np.gradient(bass_loudness))
other_delta = np.sqrt(other_loudness) + other_loudness*np.abs(np.gradient(other_loudness))
vocals_delta = np.sqrt(vocals_loudness) + vocals_loudness*np.abs(np.gradient(vocals_loudness))
drums_delta /= drums_delta.max()
bass_delta /= bass_delta.max()
other_delta /= other_delta.max()
vocals_delta /= vocals_delta.max()
import numpy as np
spec_drums = madmom.audio.spectrogram.Spectrogram(fs_drums)
spec_bass = madmom.audio.spectrogram.Spectrogram(fs_bass)
spec_other = madmom.audio.spectrogram.Spectrogram(fs_other)
spec_vocals = madmom.audio.spectrogram.Spectrogram(fs_vocals)
log_filt_spec_drums = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_drums, num_bands=8)
log_filt_spec_bass = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_bass, num_bands=8)
log_filt_spec_other = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_other, num_bands=8)
log_filt_spec_vocals = madmom.audio.spectrogram.LogarithmicFilteredSpectrogram(spec_vocals, num_bands=8)
_track1_type2 = np.loadtxt(track_path+'drums_beats_c.csv', delimiter=',')
_track2_type2 = np.loadtxt(track_path+'bass_beats_c.csv', delimiter=',')
_track3_type2 = np.loadtxt(track_path+'other_beats_c.csv', delimiter=',')
_track4_type2 = np.loadtxt(track_path+'vocals_beats_c.csv', delimiter=',')
#_track1_beats = np.loadtxt(track_path+'drums_beats_2.csv', delimiter=',')[:,0]
#_track2_beats = np.loadtxt(track_path+'bass_beats_2.csv', delimiter=',')[:,0]
#_track3_beats = np.loadtxt(track_path+'other_beats_2.csv', delimiter=',')[:,0]
#_track4_beats = np.loadtxt(track_path+'vocals_beats_2.csv', delimiter=',')[:,0]
_track1_beats = np.loadtxt(track_path+'drums_beats.csv', delimiter=',')[:,0]
_track2_beats = np.loadtxt(track_path+'bass_beats.csv', delimiter=',')[:,0]
_track3_beats = np.loadtxt(track_path+'other_beats.csv', delimiter=',')[:,0]
_track4_beats = np.loadtxt(track_path+'vocals_beats.csv', delimiter=',')[:,0]
track_length = signal_drums.shape[0]/signal_drums.sample_rate
segmentations_drums, features_drums = musicsections.segment_file(
files[1],
deepsim_model=model_deepsim,
fewshot_model=model_fewshot,
min_duration=1,
mu=0.5,
gamma=0.5,
beats_alg="madmom",
beats_file=None)
#musicsections.plot_segmentation(segmentations_drums)
###
segmentations_bass, features_bass = musicsections.segment_file(
files[0],
deepsim_model=model_deepsim,
fewshot_model=model_fewshot,
min_duration=1,
mu=0.5,
gamma=0.5,
beats_alg="madmom",
beats_file=None)
#musicsections.plot_segmentation(segmentations_bass)
###
segmentations_other, features_other = musicsections.segment_file(
files[2],
deepsim_model=model_deepsim,
fewshot_model=model_fewshot,
min_duration=1,
mu=0.5,
gamma=0.5,
beats_alg="madmom",
beats_file=None)
#musicsections.plot_segmentation(segmentations_other)
###
segmentations_vocals, features_vocals = musicsections.segment_file(
files[3],
deepsim_model=model_deepsim,
fewshot_model=model_fewshot,
min_duration=1,
mu=0.5,
gamma=0.5,
beats_alg="madmom",
beats_file=None)
#musicsections.plot_segmentation(segmentations_vocals)
drums_1to8 = np.zeros((drums_delta.shape[0], int(_track1_type2.max() - _track1_type2.min() + 1)))
bass_1to8 = np.zeros((bass_delta.shape[0], int(_track2_type2.max() - _track2_type2.min() + 1)))
other_1to8 = np.zeros((other_delta.shape[0], int(_track3_type2.max() - _track3_type2.min() + 1)))
vocals_1to8 = np.zeros((vocals_delta.shape[0], int(_track4_type2.max() - _track4_type2.min() + 1)))
last_t = 0
last_delta = 0
for i in range(0, _track1_type2.shape[0]):
current_t = _track1_beats[i]
current_delta = current_t*(drums_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track1_type2[i]
drums_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#drums_delta[int(last_delta):int(current_delta)]
#drums_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(0, _track2_type2.shape[0]):
current_t = _track2_beats[i]
current_delta = current_t*(bass_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track2_type2[i]
bass_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#bass_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(0, _track3_type2.shape[0]):
current_t = _track3_beats[i]
current_delta = current_t*(other_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track3_type2[i]
other_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#other_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(0, _track4_type2.shape[0]):
current_t = _track4_beats[i]
current_delta = current_t*(vocals_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track4_type2[i]
vocals_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#vocals_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
np.savetxt(track_path+"drums_beats_cd.csv", drums_1to8, delimiter=",")
np.savetxt(track_path+"bass_beats_cd.csv", bass_1to8, delimiter=",")
np.savetxt(track_path+"other_beats_cd.csv", other_1to8, delimiter=",")
np.savetxt(track_path+"vocals_beats_cd.csv", vocals_1to8, delimiter=",")
kernel_size = 8
kernel = np.ones(kernel_size) / kernel_size
drums_delta = np.convolve(drums_delta, kernel, mode='same')
bass_delta = np.convolve(bass_delta, kernel, mode='same')
other_delta = np.convolve(other_delta, kernel, mode='same')
vocals_delta = np.convolve(vocals_delta, kernel, mode='same')
np.savetxt(track_path+"drums_trend.csv", drums_delta, delimiter=",")
np.savetxt(track_path+"bass_trend.csv", bass_delta, delimiter=",")
np.savetxt(track_path+"other_trend.csv", other_delta, delimiter=",")
np.savetxt(track_path+"vocals_trend.csv", vocals_delta, delimiter=",")
#drums_1to8_2 = np.zeros((drums_delta.shape[0], int(_track1_type2.max() - _track1_type2.min() + 1)))
#bass_1to8_2 = np.zeros((bass_delta.shape[0], int(_track2_type2.max() - _track2_type2.min() + 1)))
#other_1to8_2 = np.zeros((other_delta.shape[0], int(_track3_type2.max() - _track3_type2.min() + 1)))
#vocals_1to8_2 = np.zeros((vocals_delta.shape[0], int(_track4_type2.max() - _track4_type2.min() + 1)))
#
#drums_1to8_2 = drums_1to8.copy()
#bass_1to8_2 = bass_1to8.copy()
#other_1to8_2 = other_1to8.copy()
#vocals_1to8_2 = vocals_1to8.copy()*2
#
#
#
#drums_1to8_2 = np.gradient(drums_1to8_2)
#
#bass_1to8_2 = np.gradient(bass_1to8_2)
#
#other_1to8_2 = np.gradient(other_1to8_2)
#
#vocals_1to8_2 = np.gradient(vocals_1to8_2)
#
#
#drums_1to8_2 = (drums_1to8_2[0]+drums_1to8_2[1]).cumsum(axis=1)/(drums_1to8_2[0]+drums_1to8_2[1]).sum()
#bass_1to8_2 = (bass_1to8_2[0]+bass_1to8_2[1]).cumsum(axis=1)/(bass_1to8_2[0]+bass_1to8_2[1]).sum()
#other_1to8_2 = (other_1to8_2[0]+other_1to8_2[1]).cumsum(axis=1)/(other_1to8_2[0]+other_1to8_2[1]).sum()
#vocals_1to8_2 = (vocals_1to8_2[0]+vocals_1to8_2[1]).cumsum(axis=1)/(vocals_1to8_2[0]+vocals_1to8_2[1]).sum()
#
#drums_1to8_2 *= (drums_1to8_2[0]+drums_1to8_2[0]).cumsum(axis=0)/(drums_1to8_2[0]+drums_1to8_2[1]).sum(axis=0)
#bass_1to8_2 *= (bass_1to8_2[0]+bass_1to8_2[0]).cumsum(axis=0)/(bass_1to8_2[0]+bass_1to8_2[1]).sum(axis=0)
#other_1to8_2 *= (other_1to8_2[0]+other_1to8_2[0]).cumsum(axis=0)/(other_1to8_2[0]+other_1to8_2[1]).sum(axis=0)
#vocals_1to8_2 *= (vocals_1to8_2[0]+vocals_1to8_2[0]).cumsum(axis=0)/(vocals_1to8_2[0]+vocals_1to8_2[1]).sum(axis=0)
#
#drums_1to8_2 /= np.sqrt(1+np.abs(drums_1to8_2))
#bass_1to8_2 /= np.sqrt(1+np.abs(bass_1to8_2))
#other_1to8_2 /= np.sqrt(1+np.abs(other_1to8_2))
#vocals_1to8_2 /= np.sqrt(1+np.abs(vocals_1to8_2))
#
#drums_1to8_2 /= ((drums_1to8_2).max()-drums_1to8_2.min())
#bass_1to8_2 /= ((bass_1to8_2).max()-bass_1to8_2.min())
#other_1to8_2 /= ((other_1to8_2).max()-other_1to8_2.min())
#vocals_1to8_2 /= ((vocals_1to8_2).max()-vocals_1to8_2.min())
#
##drums_1to8_2 += drums_1to8_2*drums_1to8.copy()
##bass_1to8_2 += bass_1to8_2*bass_1to8.copy()
##other_1to8_2 += other_1to8_2*other_1to8.copy()
##vocals_1to8_2 += vocals_1to8_2*vocals_1to8.copy()
#
#drums_1to8_2 /= np.power(1.0+np.abs(drums_1to8_2),4)
#bass_1to8_2 /= np.power(1.0+np.abs(bass_1to8_2),4)
#other_1to8_2 /= np.power(1.0+np.abs(other_1to8_2),4)
#vocals_1to8_2 /= np.power(1.0+np.abs(other_1to8_2),4)
#
#drums_1to8_2 /= 1.0*((drums_1to8_2).max()-(drums_1to8_2).min())
#bass_1to8_2 /= 1.0*((bass_1to8_2).max()-(bass_1to8_2).min())
#other_1to8_2 /= 1.5*((other_1to8_2).max()-(other_1to8_2).min())
#vocals_1to8_2 /= 1.0*((vocals_1to8_2).max()-(vocals_1to8_2).min())
#
#np.savetxt(track_path+"drums_beats_cd.csv", drums_1to8_2, delimiter=",")
#np.savetxt(track_path+"bass_beats_cd.csv", bass_1to8_2, delimiter=",")
#np.savetxt(track_path+"other_beats_cd.csv", other_1to8_2, delimiter=",")
#np.savetxt(track_path+"vocals_beats_cd.csv", vocals_1to8_2, delimiter=",")
_track1_type2 = np.loadtxt(track_path+'drums_beats_c2.csv', delimiter=',')
_track2_type2 = np.loadtxt(track_path+'bass_beats_c2.csv', delimiter=',')
_track3_type2 = np.loadtxt(track_path+'other_beats_c2.csv', delimiter=',')
_track4_type2 = np.loadtxt(track_path+'vocals_beats_c2.csv', delimiter=',')
#_track1_beats = np.loadtxt(track_path+'drums_beats_2.csv', delimiter=',')[:,0]
#_track2_beats = np.loadtxt(track_path+'bass_beats_2.csv', delimiter=',')[:,0]
#_track3_beats = np.loadtxt(track_path+'other_beats_2.csv', delimiter=',')[:,0]
#_track4_beats = np.loadtxt(track_path+'vocals_beats_2.csv', delimiter=',')[:,0]
_track1_beats = np.loadtxt(track_path+'drums_beats_2.csv', delimiter=',')[:,0]
_track2_beats = np.loadtxt(track_path+'bass_beats_2.csv', delimiter=',')[:,0]
_track3_beats = np.loadtxt(track_path+'other_beats_2.csv', delimiter=',')[:,0]
_track4_beats = np.loadtxt(track_path+'vocals_beats_2.csv', delimiter=',')[:,0]
track_length = signal_drums.shape[0]/signal_drums.sample_rate
drums_1to8 = np.zeros((drums_delta.shape[0], int(_track1_type2.max() - _track1_type2.min() + 1)))
bass_1to8 = np.zeros((bass_delta.shape[0], int(_track2_type2.max() - _track2_type2.min() + 1)))
other_1to8 = np.zeros((other_delta.shape[0], int(_track3_type2.max() - _track3_type2.min() + 1)))
vocals_1to8 = np.zeros((vocals_delta.shape[0], int(_track4_type2.max() - _track4_type2.min() + 1)))
last_t = 0
last_delta = 0
for i in range(0, _track1_type2.shape[0]):
current_t = _track1_beats[i]
current_delta = current_t*(drums_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track1_type2[i]
drums_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#drums_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(0, _track2_type2.shape[0]):
current_t = _track2_beats[i]
current_delta = current_t*(bass_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track2_type2[i]
bass_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#bass_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(0, _track3_type2.shape[0]):
current_t = _track3_beats[i]
current_delta = current_t*(other_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track3_type2[i]
other_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#other_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(0, _track4_type2.shape[0]):
current_t = _track4_beats[i]
current_delta = current_t*(vocals_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = _track4_type2[i]
vocals_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#vocals_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
np.savetxt(track_path+"drums_beats_cd_2.csv", drums_1to8, delimiter=",")
np.savetxt(track_path+"bass_beats_cd_2.csv", bass_1to8, delimiter=",")
np.savetxt(track_path+"other_beats_cd_2.csv", other_1to8, delimiter=",")
np.savetxt(track_path+"vocals_beats_cd_2.csv", vocals_1to8, delimiter=",")
#drums_1to8_2 = np.zeros((drums_delta.shape[0], int(_track1_type2.max() - _track1_type2.min() + 1)))
#bass_1to8_2 = np.zeros((bass_delta.shape[0], int(_track2_type2.max() - _track2_type2.min() + 1)))
#other_1to8_2 = np.zeros((other_delta.shape[0], int(_track3_type2.max() - _track3_type2.min() + 1)))
#vocals_1to8_2 = np.zeros((vocals_delta.shape[0], int(_track4_type2.max() - _track4_type2.min() + 1)))
#
#drums_1to8_2 = drums_1to8.copy()
#bass_1to8_2 = bass_1to8.copy()
#other_1to8_2 = other_1to8.copy()
#vocals_1to8_2 = vocals_1to8.copy()*2
#
#
#
#drums_1to8_2 = np.gradient(drums_1to8_2)
#
#bass_1to8_2 = np.gradient(bass_1to8_2)
#
#other_1to8_2 = np.gradient(other_1to8_2)
#
#vocals_1to8_2 = np.gradient(vocals_1to8_2)
#
#
#drums_1to8_2 = (drums_1to8_2[0]+drums_1to8_2[1]).cumsum(axis=1)/(drums_1to8_2[0]+drums_1to8_2[1]).sum()
#bass_1to8_2 = (bass_1to8_2[0]+bass_1to8_2[1]).cumsum(axis=1)/(bass_1to8_2[0]+bass_1to8_2[1]).sum()
#other_1to8_2 = (other_1to8_2[0]+other_1to8_2[1]).cumsum(axis=1)/(other_1to8_2[0]+other_1to8_2[1]).sum()
#vocals_1to8_2 = (vocals_1to8_2[0]+vocals_1to8_2[1]).cumsum(axis=1)/(vocals_1to8_2[0]+vocals_1to8_2[1]).sum()
#
#drums_1to8_2 *= (drums_1to8_2[0]+drums_1to8_2[0]).cumsum(axis=0)/(drums_1to8_2[0]+drums_1to8_2[1]).sum(axis=0)
#bass_1to8_2 *= (bass_1to8_2[0]+bass_1to8_2[0]).cumsum(axis=0)/(bass_1to8_2[0]+bass_1to8_2[1]).sum(axis=0)
#other_1to8_2 *= (other_1to8_2[0]+other_1to8_2[0]).cumsum(axis=0)/(other_1to8_2[0]+other_1to8_2[1]).sum(axis=0)
#vocals_1to8_2 *= (vocals_1to8_2[0]+vocals_1to8_2[0]).cumsum(axis=0)/(vocals_1to8_2[0]+vocals_1to8_2[1]).sum(axis=0)
#
#drums_1to8_2 /= np.sqrt(1+np.abs(drums_1to8_2))
#bass_1to8_2 /= np.sqrt(1+np.abs(bass_1to8_2))
#other_1to8_2 /= np.sqrt(1+np.abs(other_1to8_2))
#vocals_1to8_2 /= np.sqrt(1+np.abs(vocals_1to8_2))
#
#drums_1to8_2 /= ((drums_1to8_2).max()-drums_1to8_2.min())
#bass_1to8_2 /= ((bass_1to8_2).max()-bass_1to8_2.min())
#other_1to8_2 /= ((other_1to8_2).max()-other_1to8_2.min())
#vocals_1to8_2 /= ((vocals_1to8_2).max()-vocals_1to8_2.min())
#
##drums_1to8_2 += drums_1to8_2*drums_1to8.copy()
##bass_1to8_2 += bass_1to8_2*bass_1to8.copy()
##other_1to8_2 += other_1to8_2*other_1to8.copy()
##vocals_1to8_2 += vocals_1to8_2*vocals_1to8.copy()
#
#drums_1to8_2 /= np.power(1.0+np.abs(drums_1to8_2),4)
#bass_1to8_2 /= np.power(1.0+np.abs(bass_1to8_2),4)
#other_1to8_2 /= np.power(1.0+np.abs(other_1to8_2),4)
#vocals_1to8_2 /= np.power(1.0+np.abs(other_1to8_2),4)
#
#drums_1to8_2 /= 1.0*((drums_1to8_2).max()-(drums_1to8_2).min())
#bass_1to8_2 /= 1.0*((bass_1to8_2).max()-(bass_1to8_2).min())
#other_1to8_2 /= 1.5*((other_1to8_2).max()-(other_1to8_2).min())
#vocals_1to8_2 /= 1.0*((vocals_1to8_2).max()-(vocals_1to8_2).min())
#
#np.savetxt(track_path+"drums_beats_cd_2.csv", drums_1to8_2, delimiter=",")
#np.savetxt(track_path+"bass_beats_cd_2.csv", bass_1to8_2, delimiter=",")
#np.savetxt(track_path+"other_beats_cd_2.csv", other_1to8_2, delimiter=",")
#np.savetxt(track_path+"vocals_beats_cd_2.csv", vocals_1to8_2, delimiter=",")
_track1_type = np.loadtxt(track_path+'drums_beats_cd.csv', delimiter=',')
_track2_type = np.loadtxt(track_path+'bass_beats_cd.csv', delimiter=',')
_track3_type = np.loadtxt(track_path+'other_beats_cd.csv', delimiter=',')
_track4_type = np.loadtxt(track_path+'vocals_beats_cd.csv', delimiter=',')
_track1_type2 = np.loadtxt(track_path+'drums_beats_cd_2.csv', delimiter=',')
_track2_type2 = np.loadtxt(track_path+'bass_beats_cd_2.csv', delimiter=',')
_track3_type2 = np.loadtxt(track_path+'other_beats_cd_2.csv', delimiter=',')
_track4_type2 = np.loadtxt(track_path+'vocals_beats_cd_2.csv', delimiter=',')
_track1_flux = np.loadtxt(track_path+'superflux_drums_norm.csv', delimiter=',')
_track2_flux = np.loadtxt(track_path+'superflux_bass_norm.csv', delimiter=',')
_track3_flux = np.loadtxt(track_path+'superflux_other_norm.csv', delimiter=',')
_track4_flux = np.loadtxt(track_path+'superflux_vocals_norm.csv', delimiter=',')
drums_1to8 = np.zeros((drums_delta.shape[0], int(_track1_type2.max() - _track1_type2.min() + 1)))
bass_1to8 = np.zeros((bass_delta.shape[0], int(_track2_type2.max() - _track2_type2.min() + 1)))
other_1to8 = np.zeros((other_delta.shape[0], int(_track3_type2.max() - _track3_type2.min() + 1)))
vocals_1to8 = np.zeros((vocals_delta.shape[0], int(_track4_type2.max() - _track4_type2.min() + 1)))
last_t = 0
last_delta = 0
for i in range(len(segmentations_drums[drums_1to8.shape[1]-1][0])):
current_t = segmentations_drums[drums_1to8.shape[1]-1][0][i][1]
current_delta = current_t*(drums_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = int(segmentations_drums[drums_1to8.shape[1]-1][1][i])
drums_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#drums_delta[int(last_delta):int(current_delta)]
#drums_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(len(segmentations_bass[bass_1to8.shape[1]-1][0])):
current_t = segmentations_bass[bass_1to8.shape[1]-1][0][i][1]
current_delta = current_t*(bass_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = int(segmentations_bass[bass_1to8.shape[1]-1][1][i])
bass_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#bass_delta[int(last_delta):int(current_delta)]
#bass_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(len(segmentations_other[other_1to8.shape[1]-1][0])):
current_t = segmentations_other[other_1to8.shape[1]-1][0][i][1]
current_delta = current_t*(other_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = int(segmentations_other[other_1to8.shape[1]-1][1][i])
other_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#other_delta[int(last_delta):int(current_delta)]
#other_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
last_t = 0
last_delta = 0
for i in range(len(segmentations_vocals[vocals_1to8.shape[1]-1][0])):
current_t = segmentations_vocals[vocals_1to8.shape[1]-1][0][i][1]
current_delta = current_t*(vocals_delta.shape[0]/track_length)
#print(current_t,current_delta)
current_type = int(segmentations_vocals[vocals_1to8.shape[1]-1][1][i])
vocals_1to8[int(last_delta):int(current_delta), int(current_type)] = current_type
#vocals_delta[int(last_delta):int(current_delta)]
#vocals_1to8[int(last_delta):int(current_delta), int(current_type)] = i/10
last_t = current_t
last_delta = current_delta
_track1_type3 = _track1_type + 10.0*_track1_type2
_track2_type3 = _track2_type + 10.0*_track2_type2
_track3_type3 = _track3_type + 10.0*_track3_type2
_track4_type3 = _track4_type + 10.0*_track4_type2
_track1_type3 /= 2*((_track1_type3).max())
_track2_type3 /= 2*((_track2_type3).max())
_track3_type3 /= 2*((_track3_type3).max())
_track4_type3 /= 1.5*((_track4_type3).max())
for i in range(4):
_track1_type3[:,i] += ((1+_track1_type[:,i])*(1+drums_1to8[:,i])-1)*(1+ 0.4*(drums_delta * _track1_flux))