def plots(track):
f, axarr = plt.subplots(2, sharex=True)
path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
segments = (GETTERS.get_segments_start(h5))
sections = (GETTERS.get_sections_start(h5))
max_loudness = (GETTERS.get_segments_loudness_max(h5))
loudness = (GETTERS.get_segments_loudness_start(h5))
average_loudness = (max_loudness + loudness) / 2
average_loudness_song = GETTERS.get_loudness(h5)
start_fade_out = GETTERS.get_start_of_fade_out(h5)
end_fade_in = GETTERS.get_end_of_fade_in(h5)
pitches = GETTERS.get_segments_pitches(h5)
h5.close()
axarr[0].set_title('loudness curve for ' + ut.get_track_info(track))
axarr[0].plot(segments, average_loudness, label='Filtered')
axarr[0].axhline(average_loudness_song, color='green')
for section in enumerate(sections):
axarr[0].axvline(section[1], color='red')
axarr[0].axvline(start_fade_out, color='green')
axarr[0].axvline(end_fade_in, color='green')
idx = list()
for section in sections:
dif = segments - section
posdif = np.where(dif >=0)
idx.append(posdif[0][0])
axarr[1].set_title('Chroma values for ' + ut.get_track_info(track))
#axarr[1].set_xticks(idx,sections.astype(int))
extent =[0,segments.shape[0],0,12]
axarr[1].imshow(pitches.transpose(),extent = extent,aspect = 'auto',interpolation='nearest',origin='lower')
plt.show()
def parse_file(file, targetfile):
print "print parsing %s" %file
with open(file, 'r') as f:
global tracks, labels, features
tracks = list()
labels = list()
for line in f:
track, label = line.strip().split(' ')
tracks.append(track)
labels.append(int(label))
labels = np.array(labels)
features = np.empty((len(tracks),NUM_FEATURES),dtype='float')#hardcode number of features otherwise problem with scaling
track_info = np.empty((len(tracks)),dtype='object')
for i,track in enumerate(tracks):
#print track +' - ' +ut.get_track_info(track)
if(i%100 ==0):
print "processing track:%s\t%s" %(str(i),str(track))
if(file == SOURCE_DATA_FILE): #fetch h5 file from small dataset
h5 = GETTERS.open_h5_file_read("../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5") #fetch h5 file to allow faster preprocessing
else:
h5 = GETTERS.open_h5_file_read("../../msd_dense_subset/mood/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5") #fetch h5 file to allow faster preprocessing
track_info[i] = ut.get_track_info(track,h5)
timbre = ut.get_timbre(track,h5) #returns a tuple with 5 elements (12*5 = 60)
tempo = ut.get_tempo_feature(track,h5) #(1)
loudness = ut.get_loudness(track,h5)#returns a tuple with 3 elements (3)
energy = ut.get_energy_feature(track) #(1)
pitches = ut.get_pitches(track, h5) #(12)
features[i] = np.concatenate((timbre[0], timbre[1], timbre[2], timbre[3], timbre[4],pitches[0], pitches[1],pitches[2], pitches[3],pitches[4],np.array([tempo]),np.array(loudness),np.array([energy])))
h5.close()
print "done parsing"
print "saving data"
data = {
'features': features,
'labels': labels,
'tracks': tracks, #(songindex, songid)
'track_titles': track_info
}
with open(targetfile, 'w') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
print "data saved to %s" % targetfile
#SCRIPT
#parse_file(SOURCE_DATA_FILE_2,E_TARGET_DATA_FILE_2)
#parse_file(SOURCE_DATA_FILE,E_TARGET_DATA_FILE)
def plot_chroma(track):
path = "../../msd_dense_subset/mood/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
pitches = GETTERS.get_segments_pitches(h5)
segments = GETTERS.get_segments_start(h5)
sections = GETTERS.get_sections_start(h5)
idx = list()
for section in sections:
dif = segments - section
posdif = np.where(dif >=0)
idx.append(posdif[0][0])
plt.figure(num=None, figsize=(20, 12), dpi=80, facecolor='w', edgecolor='k')
plt.title('Chroma values for ' + ut.get_track_info(track))
plt.ylabel('Chroma values')
plt.xlabel('Time')
plt.xticks(idx,sections.astype(int))
extent =[0,segments.shape[0],0,12]
plt.imshow(pitches.transpose(), extent = extent,aspect = 'auto',interpolation='nearest',origin='lower')
plt.colorbar()
plt.autoscale(enable=True, axis='x')
#plt.pcolor(pitches.transpose())
h5.close()
plt.show()
def plot_loudness_curve(track):
path = "../../msd_dense_subset/mood/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
segments = (GETTERS.get_segments_start(h5))
sections = (GETTERS.get_sections_start(h5))
max_loudness = (GETTERS.get_segments_loudness_max(h5))
loudness = (GETTERS.get_segments_loudness_start(h5))
average_loudness = (max_loudness + loudness) / 2
average_loudness_song = GETTERS.get_loudness(h5)
start_fade_out = GETTERS.get_start_of_fade_out(h5)
end_fade_in = GETTERS.get_end_of_fade_in(h5)
plt.title('loudness curve for ' + ut.get_track_info(track))
plt.ylabel('Loudness(dB)')
plt.xlabel('Time(s)')
plt.plot(segments, average_loudness, label='Filtered')
plt.axhline(average_loudness_song, color='green',label='average')
for section in enumerate(sections):
plt.axvline(section[1], color='red')
plt.axvline(start_fade_out, color='green')
plt.axvline(end_fade_in, color='green')
#plt.legend(('average'))
h5.close()
plt.show()
