def estimate_beats(infile):
"""
Return the estimated beat onsets in seconds for an audio file.
"""
audio = standard.MonoLoader(filename=infile)()
bt = standard.BeatTrackerMultiFeature()
beats, confidence = bt(audio)
return beats
def compute_beats(audio):
"""Computes the beats using Essentia."""
logging.info("Computing Beats...")
conf = 1.0
beats, conf = ES.BeatTrackerMultiFeature()(audio)
# beats = ES.BeatTrackerDegara()(audio)
beats *= 44100 / msaf.Anal.sample_rate # Essentia requires 44100 input
# Double the beats if found beats are too little
th = 0.9 # 1 would equal to at least 1 beat per second
while beats.shape[0] / (audio.shape[0] / float(msaf.Anal.sample_rate)) < th \
and beats.shape[0] > 2:
beats = double_beats(beats)
return beats, conf
def getMultiFeatureOnsets(XAudio, Fs, hopSize):
"""
Call Essentia's implemtation of multi feature
beat tracking
:param XAudio: Numpy array of raw audio samples
:param Fs: Sample rate
:param hopSize: Hop size of each onset function value
:returns (tempo, beats): Average tempo, numpy array
of beat intervals in seconds
"""
from essentia import Pool, array
import essentia.standard as ess
X = array(XAudio)
b = ess.BeatTrackerMultiFeature()
beats = b(X)
print("Beat confidence: ", beats[1])
beats = beats[0]
tempo = 60 / np.mean(beats[1::] - beats[0:-1])
beats = np.array(np.round(beats * Fs / hopSize), dtype=np.int64)
return (tempo, beats)
def zapata14bpm(y):
essentia_beat = estd.BeatTrackerMultiFeature()
mean_tick_distance = np.mean(np.diff(essentia_beat(y)[0]))
return 60 / mean_tick_distance
def essentia_beats(audio):
beats, confidence = es.BeatTrackerMultiFeature()(audio)
return beats
def getBeats(self):
BeatTracker = es.BeatTrackerMultiFeature()
beats, _ = BeatTracker(self.audio)
return beats
def compute_beats(audio):
"""Computes the beats using Essentia."""
logging.info("Computing Beats...")
ticks, conf = ES.BeatTrackerMultiFeature()(audio)
return ticks, conf