def main():
musics = ['billiejean', 'wall']
for music in musics:
folder = '/home/hparmantier/Montreux Analytics/Git/MarkovJazzFestival/Data/'
intra = builder.IntraGraph(folder + music + '.mp3')
path = intra.simulate(save=True, filename=music + '.wav')
edge_path = nodes2edges(path)[::-1]
d = {"array": edge_path}
d["beat_duration"] = intra.beat_duration
save2json(d, music)
def test_segmentation(self, song):
song_graph = intra.IntraGraph(song)
S = song_graph.similarity_matrix(filtering=False)
seg_beats = self.segment_song(S, song_graph.nbeats)
print(seg_beats)
for i in range(len(seg_beats) - 1):
filename = 'wall_%d.wav' % i
librosa.output.write_wav(
filename,
song_graph.y[512 * song_graph.beats[seg_beats[i]]:512 *
song_graph.beats[seg_beats[i + 1]]],
song_graph.sr)
def build_aff_matrix(self):
seg_list = []
fv = [] #feature vector
song_limits = [0]
songs = []
all_beats = []
c = 0
shift = 0
for filename in os.listdir(self.directory):
if filename.endswith(".mp3"):
print(filename)
song_graph = intra.IntraGraph(self.directory + filename)
song_limits.append(song_limits[-1] + song_graph.nbeats)
#print(song_limits)
features = song_graph.beat_features()
seg = self.segment_song(song_graph.similarity_matrix(),
song_graph.nbeats)
for i in range(len(seg) - 1):
print(i)
mean_feat = np.mean(features[:, seg[i]:seg[i + 1]], axis=1)
fv.append(mean_feat)
self.song_seg.append((c, i))
song_graph.seg = seg
songs.append(song_graph)
all_beats.append(seg + shift * np.ones(len(seg)))
shift += song_graph.nbeats
c += 1
if c >= self.max_songs:
break
featvec = np.transpose(np.asarray(fv))
#pdb.set_trace()
aff = librosa.segment.recurrence_matrix(featvec,
width=1,
metric='euclidean',
sym=True,
mode='affinity')
self.songs = songs
self.nsongs = len(songs)
self.song_limits = song_limits
self.all_beats = [
int(item) for sublist in all_beats for item in sublist
]
print(self.all_beats)
return aff
def init():
folder = '/home/hparmantier/Montreux Analytics/Git/MarkovJazzFestival/Data/'
#musics = [os.path.splitext(f)[0] for f in os.listdir(folder)]
musics = ['Jamiroquai-Cosmic Girl']
for i, music in enumerate(musics):
print("########### " + music + " ############")
print("########### " + str(i + 1) + "/" + str(len(musics)) +
" #########")
intra = builder.IntraGraph(folder + music + '.mp3')
path = intra.simulate(save=True, filename=music + '.wav')
edge_path = nodes2edges(path)[::-1]
d = {"array": edge_path} ##
d["beat_duration"] = intra.beat_duration ##
save2json(d, music) ##
neo = writer.connect_to_db()
print("Pushing to Neo4j...")
writer.intra_neo_from_nx(intra.G, edge_path, neo, music)
print("Done")
def print_duration():
folder = '/home/hparmantier/Montreux Analytics/Git/MarkovJazzFestival/Data/'
musics = ['together']
for music in musics:
intra = builder.IntraGraph(folder + music + '.mp3')
print(intra.beat_duration)
def check_duration():
folder = '/home/hparmantier/Montreux Analytics/Data/musics/inter/'
musics = [os.path.splitext(f)[0] for f in os.listdir(folder)]
for music in musics:
intra = builder.IntraGraph(folder + music + '.mp3')
print(music + ': ' + str(intra.beat_duration))