def getStartPoint(input_phrase, match):
proc = dbt.DBNDownBeatTrackingProcessor(beats_per_bar=[4, 4], fps=100)
act = dbt.RNNDownBeatProcessor()(input_phrase)
in_down_index = np.argmin(proc(act)[:, 1])
#print(proc(act))
print("input downbeat:{}".format(in_down_index))
in_down_time = proc(act)[in_down_index, 0] * 1000
act2 = dbt.RNNDownBeatProcessor()(match)
match_down_index = np.argmin(proc(act2)[:, 1])
#print(proc(act2))
print("match downbeat:{}".format(match_down_index))
match_down_time = proc(act2)[match_down_index, 0] * 1000
return in_down_time, match_down_time
def get_bars_from_audio(song):
"""
Returns the bars of a song, directly from its audio signal.
Encapsulates the downbeat estimator from the madmom toolbox [1].
Parameters
----------
song : String
Path to the desired song.
Returns
-------
downbeats_times : list of tuple of float
List of the estimated bars, as (start, end) times.
References
----------
[1] Böck, S., Korzeniowski, F., Schlüter, J., Krebs, F., & Widmer, G. (2016, October).
Madmom: A new python audio and music signal processing library.
In Proceedings of the 24th ACM international conference on Multimedia (pp. 1174-1178).
"""
act = dbt.RNNDownBeatProcessor()(song)
proc = dbt.DBNDownBeatTrackingProcessor(beats_per_bar=4, fps=100)
song_beats = proc(act)
downbeats_times = []
if song_beats[0][1] != 1:
downbeats_times.append(0.1)
for beat in song_beats:
if beat[1] == 1:
downbeats_times.append(beat[0])
mean_bar = np.mean([
downbeats_times[i + 1] - downbeats_times[i]
for i in range(len(downbeats_times) - 1)
])
signal_length = act.shape[0] / 100
while downbeats_times[-1] + 1.1 * mean_bar < signal_length:
downbeats_times.append(round(downbeats_times[-1] + mean_bar, 2))
downbeats_times.append(signal_length)
return frontiers_to_segments(downbeats_times)
def chroma_and_spectral(loop):
######## madmom
# downbeat
proc = dbt.DBNDownBeatTrackingProcessor(beats_per_bar=[4, 4], fps=100)
act = dbt.RNNDownBeatProcessor()(loop)
#print(proc(act)[:,0]) #time v.s. index in a bar
beat_frames = librosa.time_to_frames(proc(act)[:, 0],
sr=44100,
hop_length=512)
# to get downbeat index and segment num
# filter music with few bar
if np.sum(proc(act)[:, 1] == 1) < 3:
return False, 0, 0, 0, 0, 0
downbeat_first = np.argmin(proc(act)[:, 1])
#bug for [1,5,9....] there is a beat before
if (beat_frames[0] != 0):
downbeat_first = downbeat_first + 1
bar_num = math.floor((len(beat_frames) - downbeat_first) / 4)
'''
# chroma
pcp=ch.CLPChromaProcessor(fps=100)
chroma=pcp(loop)
chroma=chroma.T #madmom form is different from librosa (frames,12(madmom)) <-> (12,frames(lib))
'''
y, sr = librosa.load(loop, sr=44100) # sr=sample rate
y_harmonic, _ = librosa.effects.hpss(y)
# beatsynchronous chromagrams.
chroma = librosa.feature.chroma_cqt(y=y_harmonic, sr=sr, n_chroma=12)
beat_chroma = librosa.util.sync(chroma, beat_frames, aggregate=np.median)
#tempo
tempo = get_tempo(loop)
#spec
beat_spectral = 0
'''
librosa.display.specshow(beat_chroma,x_axis='chroma')
plt.title('chromagram(madmom)')
plt.savefig("chroma(madmom).png")
'''
'''
######## librosa
y, sr = librosa.load(loop, sr=44100) # sr=sample rate
#print(sr)
y_harmonic, y_percussive = librosa.effects.hpss(y)
tempo, beat_frames = librosa.beat.beat_track(y=y_percussive ,sr=sr)
print(beat_frames.shape,tempo)
# beatsynchronous chromagrams.
chromagram = librosa.feature.chroma_cqt(y=y_harmonic,sr=sr,n_chroma=12)
# We'll use the median value of each feature between beat frames
beat_chroma = librosa.util.sync(chromagram,
beat_frames,
aggregate=np.median)
# beatsynchronous spectralgrams.
#n_mel?????
S=librosa.feature.melspectrogram(y=y,sr=sr,n_mels=7)
beat_spectral = librosa.util.sync(S,
beat_frames,
aggregate=np.median)
librosa.display.specshow(beat_spectral,y_axis='mel')
plt.xlabel('beat')
#plt.colorbar(format='%+2.0f dB')
#plt.colorbar()
plt.title('Spectrogram')
plt.savefig(loop+"_spectral_12.png")
librosa.display.specshow(beat_chroma,y_axis='chroma')
plt.xlabel('beat')
plt.colorbar()
plt.title('Chromagram')
plt.savefig(loop+"_chroma_12.png")
librosa.display.specshow(chromagram,y_axis='chroma')
plt.xlabel('frame')
plt.title('Chromagram(no_beat_sync)')
plt.savefig(loop+"_chroma_nosyc.png")
'''
return True, beat_chroma, beat_spectral, tempo, downbeat_first, bar_num
hop_length=hop_length)
plt.plot(times, librosa.util.normalize(onset_env), label='Onset strength')
plt.vlines(times[beats],
0,
1,
alpha=0.5,
color='r',
linestyle='--',
label='Beats')
print(times[beats])
#### madmom
#proc = BeatTrackingProcessor(fps=100)
#proc = bt.DBNBeatTrackingProcessor(fps=100)
#act = bt.RNNBeatProcessor()(sample2)
proc = dbt.DBNDownBeatTrackingProcessor(beats_per_bar=[4, 4], fps=120)
act = dbt.RNNDownBeatProcessor()(sample)
plt.vlines(proc(act),
0,
1,
alpha=0.5,
color='g',
linestyle='--',
label='Beats_2')
print(proc(act))
plt.legend(frameon=True, framealpha=0.75)
# Limit the plot to a 15-second window
plt.xlim(0, 15)
plt.gca().xaxis.set_major_formatter(librosa.display.TimeFormatter())
plt.tight_layout()