def estimate(cls, audio):
if audio.is_mono():
signal = MonoLoader(filename=audio.filename)()
else:
signal = StereoLoader(filename=audio.filename)()
extractor = RhythmExtractor2013(method="multifeature")
bpm, *_ = extractor(signal)
return bpm
def f_beats_for_minutes(Audio):
## FUNCION PARA EXTRAER LOS BEATS POR MINUTOS DEL ARCHIVO
## CON ESTO PODREMOS LUEGO VALIDAR LOS COMPACES
rhythm_extractor = RhythmExtractor2013(method="multifeature")
bpm, beats, b_conf, _, _ = rhythm_extractor(Audio)
#print("BPM:", bpm)
#print ("Primer Beat en el segundo ->", beats[0] )
#print ("Ultimo Beat en el segundo ->", beats[len(beats)-1] )
#print("beats Detectados ->", len(beats) )##picos de grafica
#print("Beat positions (sec.):", beats)
#print("Beat estimation confidence:", b_conf)
return bpm
def lowLevel(songName):
global dataset
global lock
print songName
key = re.sub(r',', "", songName.split('/')[-1])
#IF already present in dataset dont extract
if dataset.has_key(key):
feature = dataset[key]
return feature
else:
#Loading song and using Extractors
loader = MonoLoader(filename=songName)
audio = loader()
extractor = LowLevelSpectralEqloudExtractor()
feature = list(extractor(audio))
del feature[1]
del feature[1]
extractor = LowLevelSpectralExtractor()
featureTwo = list(extractor(audio))
del featureTwo[0]
del featureTwo[-2]
featureTwo[4] = feature[4][1]
feature.extend(featureTwo)
extractor = Loudness()
feature.append(extractor(audio))
extractor = LogAttackTime()
feature.append(extractor(audio)[0])
extractor = KeyExtractor()
feature.append(extractor(audio)[2])
extractor = RhythmExtractor2013()
data = extractor(audio)
feature.append(data[0])
feature.append(data[2])
for x in range(len(feature)):
if type(feature[x]) is np.ndarray:
feature[x] = avg(feature[x])
arr = key + "," + str(feature)[1:-1] + "\n"
f = open('data.csv', 'a')
lock.acquire()
f.write(arr)
lock.release()
f.close()
return feature
def get_song_bpm(audio, params={}) -> Tuple[float, float]:
"""Extracts the BPM for the given monoloaded audio file
Parameters
----------
audio
Monoloaded audio to be analyzed
params
RhythmExtractor2013 parameters
Returns
-------
Tuple[float, float]
A tuple of the BPM and a confidence of that BPM
"""
rhythm_extractor = RhythmExtractor2013(**params)
bpm, _, beats_confidence, _, _ = rhythm_extractor(audio)
return bpm, beats_confidence