def process_song(i, dataset):
"""
Returns features for all windows of a given song in the dataset
(to be run in parallel)
"""
score = dataset.get_pianoroll(
i, score_type=['precise_alignment', 'broad_alignment'], resolution=RES)
audio, sr = dataset.get_audio(i)
audio = esst.Resample(inputSampleRate=sr, outputSampleRate=SR)(audio)
return get_song_win_features(score, audio)
def compute(self, *args):
from math import pi
x = args[1]
for frame in es.FrameGenerator(x,
frameSize=self._frameSize,
hopSize=self._hopSize,
startFromZero=True):
y = []
s = int(self._frameSize / 2 -
self._hopSize / 2) - 1 # consider non overlapping case
e = int(self._frameSize / 2 + self._hopSize / 2)
# Stage 1: Attenuation. Is not required because we are using float point.
# Stage 2: Resample
yResample = es.Resample(inputSampleRate=self._sampleRate,
outputSampleRate=self._sampleRateOver,
quality=self._quality)(frame)
# Stage 3: Emphasis
if self._emphatise:
fPole = 20e3 # Hz
fZero = 14.1e3
rPole = fPole / self._sampleRateOver
rZero = fZero / self._sampleRateOver
yEmphasis = es.IIR(denominator=esarr([1., rPole]),
numerator=esarr([1., -rZero]))(yResample)
else:
yEmphasis = yResample
# Stage 4 Absolute
yMaxArray = np.abs(yEmphasis)
# Stage 5 optional DC Block
if self._BlockDC:
yDCBlocked = es.DCRemoval(sampleRate=self._sampleRate,
cutoffFrequency=1.)(yEmphasis)
yAbsoluteDCBlocked = np.abs(yDCBlocked)
yMaxArray = np.maximum(yMaxArray, yAbsoluteDCBlocked)
y = [
((i + self._idx * self._hopSize) / float(self._sampleRateOver),
yMax) for i, yMax in enumerate(yMaxArray)
if yMax > self._clippingThreshold
]
self._idx += 1
return esarr(y)
def apply_replay_gain(float_signal, sample_rate):
'''
Normalizes perceived loudness af an audio signal.
Calculates a replay gain value and applies this gain to the input.
Returns normalized signal and the replay gain calculated.
'''
downsampled_signal = es.Resample(inputSampleRate=sample_rate,
outputSampleRate=8000)(float_signal)
replay_gain_dB = es.ReplayGain(sampleRate=8000)(downsampled_signal)
gain = np.power(10, replay_gain_dB / 20)
return np.array(float_signal) * gain, replay_gain_dB
def analyze_misc(filename, segment_duration=20):
# Compute replay gain and duration on the entire file, then load the
# segment that is centered in time with replaygain applied
audio = es.MonoLoader(filename=filename)()
replaygain = es.ReplayGain()(audio)
segment_start = (len(audio) / 44100 - segment_duration) / 2
segment_end = segment_start + segment_duration
if segment_start < 0 or segment_end > len(audio) / 44100:
raise ValueError(
'Segment duration is larger than the input audio duration')
loader = es.EasyLoader(filename=filename,
replayGain=replaygain,
startTime=segment_start,
endTime=segment_end)
windowing = es.Windowing(type='blackmanharris62')
spectrum = es.Spectrum()
powerspectrum = es.PowerSpectrum()
centroid = es.Centroid()
zcr = es.ZeroCrossingRate()
rms = es.RMS()
hfc = es.HFC()
pool = essentia.Pool()
audio = loader()
for frame in es.FrameGenerator(audio, frameSize=2048, hopSize=1024):
frame_spectrum = spectrum(windowing(frame))
pool.add('rms', rms(frame))
pool.add('rms_spectrum', rms(frame_spectrum))
pool.add('hfc', hfc(frame_spectrum))
pool.add('spectral_centroid', centroid(frame_spectrum))
pool.add('zcr', zcr(frame))
audio_st, sr, _, _, _, _ = es.AudioLoader(filename=filename)()
# Ugly hack because we don't have a StereoResample
left, right = es.StereoDemuxer()(audio_st)
resampler = es.Resample(inputSampleRate=sr, outputSampleRate=44100)
left = resampler(left)
right = resampler(right)
audio_st = es.StereoMuxer()(left, right)
audio_st = es.StereoTrimmer(startTime=segment_start,
endTime=segment_end)(audio_st)
ebu_momentary, _, _, _ = es.LoudnessEBUR128(hopSize=1024 / 44100,
startAtZero=True)(audio_st)
pool.set('ebu_momentary', ebu_momentary)
return pool
def load_audio(path, sample_rate, mono=True):
"""
Load an audio file using Essentia
:param path: (str) location of audio file to load
:param sample_rate: (int) sampling rate to load audio at
:param mono: (bool) convert file to mono, defaults to True
:return: audio samples
"""
# Load audio file
loader = es.AudioLoader(filename=path)
results = loader()
samples = results[0]
orig_rate = results[1]
channels = results[2]
# Make sure we get a mono or stereo audio
if channels > 2:
raise RuntimeError("Can't handle more than two audio channels.")
# If there is only one channel, duplicate the first over to the second.
# Essentia always loads as a stereo audio file and the right channel is
# all zeros in this case. We'll convert to a stereo file for some of the
# processing here such as the Loudness Normalization.
if channels == 1:
samples[:, 1] = samples[:, 0]
# Mix to mono if required
if mono:
samples = mix_to_mono(samples)
# Perform resampling if required
if orig_rate != sample_rate:
resample = es.Resample(inputSampleRate=orig_rate,
outputSampleRate=sample_rate)
# Resampling for a stereo audio file
if not mono:
resampled_left = resample(samples[:, 0])
resampled_right = resample(samples[:, 1])
samples = np.array([resampled_left, resampled_right])
samples = samples.T
# Resampling for a mono audio file
else:
samples = resample(samples)
return samples, channels
def resample_audio(self, target_sample_rate):
"""Downsample a audio into a target sample rate
Arguments:
target_sample_rate {[type]} -- [description]
Raises:
ValueError: If `target_sample_rate` is less than the sample rate of given audio data.
Returns:
[type] -- [description]
"""
if target_sample_rate > self.fs:
raise ValueError("Target_sample_rate should be lower than %s" %
self.fs)
resampler = estd.Resample(inputSampleRate=self.fs,
outputSampleRate=target_sample_rate,
quality=1)
return resampler.compute(self.audio_vector)
def resample_audio(self, target_sample_rate):
"""Downsample a audio into a target sample rate"""
if target_sample_rate > self.fs:
raise ValueError("Target_sample_rate should be lower than %s" % self.fs)
resampler = estd.Resample(inputSampleRate=self.fs, outputSampleRate=target_sample_rate, quality=1)
return resampler.compute(self.audio_vector)