def _ExtractAll(self, audio_location, sample_rate):
# Build a dataflow object using FeaturePlan
fp = FeaturePlan(sample_rate=sample_rate)
# Using *.addFeature() multiple extractions can be called with a
# single call
fp.addFeature('zcr: ZCR')
fp.addFeature('mfcc: MFCC')
fp.addFeature('mfcc_D1: MFCC > Derivate DOrder=1')
#fp.addFeature('mfcc_D2: MFCC > Derivate DOrder=2')
fp.addFeature('flux: SpectralFlux')
fp.addFeature('energy: Energy')
fp.addFeature('loudness: Loudness')
fp.addFeature('obsi: OBSI')
fp.addFeature('sharpness: PerceptualSharpness')
fp.addFeature('spread: PerceptualSpread')
fp.addFeature('rolloff: SpectralRolloff')
fp.addFeature('variation: SpectralVariation')
# Get dataflow
df = fp.getDataFlow()
# Configure engine
engine = Engine()
engine.load(df)
# extract features from audio using AudioFileProcessor
afp = AudioFileProcessor()
afp.processFile(engine, audio_location)
# features array holds all the extracted features
features = engine.readAllOutputs()
#print features["zcr"]
# returns the array of features extracted
return features
def _energy(audio_location, sample_rate):
# This function behaves the same as 'python yaafe.py -r SAMPLERATE -f \
# "energy: Energy PARAMETERS" WAV-LOCATION'
# SAMPLERATE : Samplerate of the file being processed
#- blockSize (default=1024): output frames size
#- stepSize (default=512): step between consecutive frames
# Build a dataflow object using FeaturePlan
# blockSize, stepSize could be added too. 1024, 512 default
fp = FeaturePlan(sample_rate=sample_rate)
# Using *.addFeature() multiple extractions can be called with a
# single call
fp.addFeature('Energy: Energy')
#('energy: Energy blockSize=1024 stepSize=512')
# Get dataflow
df = fp.getDataFlow()
# Configure engine
engine = Engine()
engine.load(df)
# extract features from audio using AudioFileProcessor
afp = AudioFileProcessor()
afp.processFile(engine, audio_location)
# features array holds all the extracted features
features = engine.readAllOutputs()
# returns the array of features extracted
return features
def main(argv):
if len(argv) != 2:
print 'usage: python %s foo.mp3' % (argv[0])
return
fp = FeaturePlan(sample_rate=SAMPLE_RATE,
resample=True,
time_start=TIME_START,
time_limit=TIME_LIMIT)
fp.addFeature("frames: Frames blockSize={0} stepSize={1}".format(
SLICE_WINSIZE, SLICE_STEPSIZE))
if 'YAAFE_PATH' in os.environ:
fp.addFeature(
"beat_hist: BeatHistogramSummary ACPNbPeaks=3 BHSBeatFrameSize=128 BHSBeatFrameStep=64 "
"BHSHistogramFrameSize=40 BHSHistogramFrameStep=40 FFTLength=0 FFTWindow=Hanning "
"HInf=40 HNbBins=80 HSup=200 NMANbFrames=5000 blockSize=1024 stepSize=512"
)
df = fp.getDataFlow()
engine = Engine()
engine.load(df)
afp = AudioFileProcessor()
song_path = argv[1]
assert os.path.exists(song_path)
afp.processFile(engine, song_path)
frames = engine.readOutput('frames')
frames = np.concatenate(frames)
print 'time start: %ss' % TIME_START
print 'time limit: %ss' % TIME_LIMIT
print 'duration:', 1. * frames.size / SAMPLE_RATE
if 'YAAFE_PATH' in os.environ:
beat_hist = engine.readOutput('beat_hist')
print 'beat_hist: %s' % beat_hist
def __init__(self, **args):
self.engine = Engine()
self.afp = AudioFileProcessor()
self.block_size = (1024
if not 'block_size' in args else args['block_size'])
self.step_size = (512
if not 'step_size' in args else args['step_size'])
def compute_spec(song_path):
fp = FeaturePlan(sample_rate=22050, resample=True)
#add one feature
fp.addFeature("spec: PowerSpectrum blockSize=1024 stepSize=512")
df = fp.getDataFlow()
engine = Engine()
engine.load(df)
afp = AudioFileProcessor()
afp.processFile(engine, song_path)
return engine.readOutput('spec')
def initialize(self, feature_dict):
""" Run the required boilerplate for yaafe """
self.feature_dict = feature_dict
self.fp = FeaturePlan(sample_rate=self.sample_rate, normalize=0.98)
for name, desc in self.feature_dict.items():
self.fp.addFeature("{0}: {1}".format(name, desc))
self.df = self.fp.getDataFlow()
self.engine = Engine()
self.engine.load(self.df)
self.afp = AudioFileProcessor()
return self
def _ExtractZCRAndFlux(self, audio_location, sample_rate):
fp = FeaturePlan(sample_rate=sample_rate)
fp.addFeature('zcr: ZCR')
fp.addFeature('flux: SpectralFlux')
df = fp.getDataFlow()
engine = Engine()
engine.load(df)
afp = AudioFileProcessor()
afp.processFile(engine, audio_location)
features = engine.readAllOutputs()
return features
def startEngine(path):
global features
engine = Engine()
engine.load(df)
afp = AudioFileProcessor()
afp.processFile(engine,path)
features = engine.readAllOutputs()
return 'Yaafe引擎启动成功'
def init():
global engine
fp = FeaturePlan(sample_rate=44100, resample=True, time_start=0,time_limit=20)
fp.addFeature("loudness: Loudness")
fp.addFeature("perceptualSharpness: PerceptualSharpness")
fp.addFeature("perceptualSpread: PerceptualSpread")
fp.addFeature("obsi: OBSI")
fp.addFeature("obsir: OBSIR")
df = fp.getDataFlow()
engine = Engine() # Engine setup
engine.load(df)
return 'initialization'
def _zcr(self, audio_location, sample_rate):
# This function behave the same as 'python yaafe.py -r SAMPLERATE -f \
# "zcr: ZCR blockSize=1024 stepSize=512" WAV-LOCATION'
# SAMPLERATE = samplerate of the file being processed
# zcr = name for the process (zcr1, zcr2... )
# ZCR = the feature that is being extracted
# blockSize = output frames size
# stepSize = step between consecutive frames
# Build a dataflow object using FeaturePlan
# blockSize, stepSize could be added too. 1024, 512 default
fp = FeaturePlan(sample_rate=sample_rate)
# Using *.addFeature() multiple extractions can be called with a
# single call
fp.addFeature('ZCR: ZCR')
# Get dataflow
df = fp.getDataFlow()
# Or load it from a file
# df = DataFlow()
# df.load(dataflow_file)
# Configure engine
engine = Engine()
engine.load(df)
# extract features from audio using AudioFileProcessor
afp = AudioFileProcessor()
afp.processFile(engine, audio_location)
# features array holds all the extracted features
features = engine.readAllOutputs()
# extract features from an audio file and write results to csv files
# afp.setOutputFormat('csv','output',{'Precision':'8'})
# afp.processFile(engine,audiofile)
# this creates output/myaudio.wav.mfcc.csv,
# output/myaudio.wav.mfcc_d1.csv and
# output/myaudio.wav.mfcc_d2.csv files.
# Clear the engine so it can be used again
#engine.reset()
# returns the array of features extracted
return features
def init():
global engine
fp = FeaturePlan(sample_rate=44100, resample=True, time_start=0, time_limit=60) # 采样率44.1Hkz，提取20 - 40s
fp.addFeature("mfcc: MFCC") # 梅尔倒谱系数 13
fp.addFeature("energy: Energy") # 短时能量 1
fp.addFeature("zcr: ZCR") # 短时平均过零率 1
fp.addFeature("sf: SpectralFlux") # 尖锐度 1
fp.addFeature("sr: SpectralRolloff") # 频谱滚降点 1
fp.addFeature("lpc: LPC LPCNbCoeffs=3") # 线性预测编码 3
fp.addFeature("lx: Loudness") # 响度 24
df = fp.getDataFlow()
engine = Engine() # 配置Engine
engine.load(df)
return 'Yaafe初始化'
def main(audio_file, variables_to_extract):
# Delete old files and recriate folder
if isdir('output'): shutil.rmtree('output')
os.mkdir('output')
# Build a DataFlow object using FeaturePlan
fp = FeaturePlan(sample_rate = 44100)
for variable in variables_to_extract:
fp.addFeature(feat_json['variables'][variable])
df = fp.getDataFlow()
# configure an Engine
engine = Engine()
engine.load(df)
# Make csv files with audio variables
process_audio(audio_file, engine)
return
def _SpectralFlux(self, audio_location, sample_rate):
# This function behaves the same as 'python yaafe.py -r SAMPLERATE -f \
# "flux: SpectralFlux PARAMETERS" WAV-LOCATION'
# SAMPLERATE : Samplerate of the file being processed
# - FFTLength (default=0): Frame's length on which perform FFT. Original
# frame is padded with zeros or truncated to reach this size. If 0 then
# use original frame length.
# - FFTWindow (default=Hanning): Weighting window to apply before fft. Hanning|Hamming|None
# - FluxSupport (default=All): support of flux computation. if 'All' then
# use all bins (default), if 'Increase' then use only bins which are increasing
# - blockSize (default=1024): output frames size
# - stepSize (default=512): step between consecutive frames
# Build a dataflow object using FeaturePlan
fp = FeaturePlan(sample_rate=sample_rate)
# Using *.addFeature() multiple extractions can be called with a
# single call
fp.addFeature('Flux: SpectralFlux')
#('flux: SpectralFlux FFTLength=0 FFTWindow=Hanning FluxSupport=All\
# blockSize=1024 stepSize=512')
# Get dataflow
df = fp.getDataFlow()
# Configure engine
engine = Engine()
engine.load(df)
# extract features from audio using AudioFileProcessor
afp = AudioFileProcessor()
afp.processFile(engine, audio_location)
# features array holds all the extracted features
features = engine.readAllOutputs()
# returns the array of features extracted
return features
def _mfcc(self, audio_location, sample_rate):
# This function behaves the same as 'python yaafe.py -r SAMPLERATE -f \
# "mfcc: MFCC PARAMETERS" WAV-LOCATION'
# SAMPLERATE : Samplerate of the file being processed
#- CepsIgnoreFirstCoeff (default=1): 0 keeps the first cepstral coeffcient, 1 ignore it
#- CepsNbCoeffs (default=13): Number of cepstral coefficient to keep.
#- FFTWindow (default=Hanning): Weighting window to apply before fft. Hanning|Hamming|None
#- MelMaxFreq (default=6854.0): Maximum frequency of the mel filter bank
#- MelMinFreq (default=130.0): Minimum frequency of the mel filter bank
#- MelNbFilters (default=40): Number of mel filters
#- blockSize (default=1024): output frames size
#- stepSize (default=512): step between consecutive frames
# Build a dataflow object using FeaturePlan
fp = FeaturePlan(sample_rate=sample_rate)
# Using *.addFeature() multiple extractions can be called with a
# single call
fp.addFeature('mfcc: MFCC')
#('mfcc: MFCC CepsIgnoreFirstCoeff=0 \
#CepsNbCoeffs=13 FFTWindow=Hanning MelMaxFreq=6854\
#MelMinFreq=130 MelNbFilters=40 blockSize=1024 stepSize=512')
# Get dataflow
df = fp.getDataFlow()
engine = Engine()
engine.load(df)
# extract features from audio using AudioFileProcessor
afp = AudioFileProcessor()
afp.processFile(engine, audio_location)
# features array holds all the extracted features
features = engine.readAllOutputs()
# returns the array of features extracted
return features
def __init__(self,
num_bands,
fmin,
num_octaves,
fps,
align,
log_div,
sample_rate=44100,
fold=None):
self.fps = fps
self.num_bands = num_bands
self.align = align
self.fmin = fmin
self.num_octaves = num_octaves
self.log_div = log_div
self.sample_rate = sample_rate
from yaafelib import FeaturePlan, Engine
fp = FeaturePlan(sample_rate=sample_rate)
cqt_config = " ".join([
'cqt: CQT', 'CQTAlign={}'.format(align),
'CQTBinsPerOctave={}'.format(num_bands),
'CQTMinFreq={}'.format(fmin),
'CQTNbOctaves={}'.format(num_octaves),
'stepSize={}'.format(sample_rate / fps)
])
fp.addFeature(cqt_config)
df = fp.getDataFlow()
self.engine = Engine()
self.engine.load(df)
""" Stand-alone application to demonstrate yaafe's transformations """
import numpy as np
import matplotlib.pyplot as plt
from yaafelib import FeaturePlan, Engine, AudioFileProcessor
FPLAN = FeaturePlan(sample_rate=44100)
FPLAN.addFeature("mfcc: MFCC blockSize=512 stepSize=128")
FPLAN.addFeature("mfcc_stat: MFCC blockSize=512 stepSize=128 > "
"StatisticalIntegrator NbFrames=40 StepNbFrames=8")
ENGINE = Engine()
ENGINE.load(FPLAN.getDataFlow())
PROCESSOR = AudioFileProcessor()
PROCESSOR.processFile(ENGINE, 'track.wav')
DATA = ENGINE.readAllOutputs()
ENGINE.flush()
X_MFCC = DATA['mfcc']
X_MFCC_STAT = DATA['mfcc_stat']
MFCC_DESC = list()
for i in range(1, 14):
desc = "Average for Band {}".format(i)
MFCC_DESC.append(desc)
for i in range(1, 14):
desc = "Standard Dev. for Band {}".format(i)
MFCC_DESC.append(desc)
plt.ion()
FIG = plt.figure()
FIG.set_size_inches(14, 8)
def detect(wav_path, ans_path=None):
sample_rate = 8000
block_size = 1024
step_size = block_size / 2
n_band = block_size / 2
freq_bound = [i * sample_rate / 2. / n_band for i in range(n_band + 1)]
plan = FeaturePlan(sample_rate=sample_rate, resample=True)
plan.addFeature('power_spectrum: PowerSpectrum blockSize=%d stepSize=%d' %
(block_size, step_size))
dataflow = plan.getDataFlow()
afp = AudioFileProcessor()
engine = Engine()
engine.load(dataflow)
afp.processFile(engine, wav_path)
spectrogram = engine.readOutput('power_spectrum')
seq = []
for spectrum in spectrogram:
mean_mag = np.mean(spectrum)
if mean_mag <= SILENT_MAG_THRESHOLD:
seq.append(SILENT_KEY)
continue
lower_data = (-1, -1)
upper_data = (-1, -1)
for target_idx, target_freq in itertools.chain(enumerate(LOWER_FREQS),
enumerate(UPPER_FREQS)):
idx = bisect.bisect(freq_bound, target_freq)
assert idx > 0
freq1 = freq_bound[idx - 1]
mag1 = spectrum[idx - 1]
freq2 = freq_bound[idx]
mag2 = spectrum[idx]
w1 = 1. * (freq2 - target_freq) / (freq2 - freq1)
w2 = 1. - w1
target_mag = (w1 * mag1 + w2 * mag2)
if target_mag > mean_mag * 2:
if target_freq < 1000:
if target_mag > lower_data[1]:
lower_data = (target_idx, target_mag)
else:
if target_mag > upper_data[1]:
upper_data = (target_idx, target_mag)
lower_idx = lower_data[0]
upper_idx = upper_data[0]
if lower_idx == -1 or upper_idx == -1:
seq.append(UNKNOWN_KEY)
else:
seq.append(KEYS[lower_idx * len(LOWER_FREQS) + upper_idx])
ans = purge_seq(seq)
if ans_path is not None:
with open(ans_path) as fh:
for i, line in enumerate(fh):
line = line.strip()
assert line == ans[i], "%s != %s" % (line, ans[i])
print '[%d] %s' % (i, line)
else:
for i, line in enumerate(ans):
print '[%d] %s' % (i, line)
