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:", 1.0 * frames.size / SAMPLE_RATE
if "YAAFE_PATH" in os.environ:
beat_hist = engine.readOutput("beat_hist")
print "beat_hist: %s" % beat_hist
class MFCCExtractor:
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 extract(self, path, **args):
self.path = path
print path
self.wave = wave.open(path, 'r')
self.rate = self.wave.getframerate()
fp = FeaturePlan(sample_rate=self.rate)
fp.addFeature('mfcc: MFCC blockSize={} stepSize={}'.format(self.block_size, self.step_size))
self.engine.load(fp.getDataFlow())
self.afp.processFile(self.engine, path)
feats = self.engine.readAllOutputs()
if 'save_to_disk' in args:
self.__save_to_disk(feats['mfcc'])
return feats['mfcc']
def __save_to_disk(self, feats):
new_path = self.path + ".mfc.csv"
np.savetxt(new_path, feats, delimiter=",")
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
class MFCCExtractor:
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 extract(self, path, **args):
self.path = path
print path
self.wave = wave.open(path, 'r')
self.rate = self.wave.getframerate()
fp = FeaturePlan(sample_rate=self.rate)
fp.addFeature('mfcc: MFCC blockSize={} stepSize={}'.format(
self.block_size, self.step_size))
self.engine.load(fp.getDataFlow())
self.afp.processFile(self.engine, path)
feats = self.engine.readAllOutputs()
if 'save_to_disk' in args:
self.__save_to_disk(feats['mfcc'])
return feats['mfcc']
def __save_to_disk(self, feats):
new_path = self.path + ".mfc.csv"
np.savetxt(new_path, feats, delimiter=",")
def startEngine(path):
global afp, features
afp = AudioFileProcessor()
afp.processFile(engine,path)
features = engine.readAllOutputs() # matrix of all features
return 'extracted'
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 startEngine(path):
global afp, features
afp = AudioFileProcessor()
afp.processFile(engine,path) # 从mp3文件中提取特征，它必需提供engine配置
features = engine.readAllOutputs() # 得到所有特征矩阵
return 'Yaafe提取成功'
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 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 _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__(self):
self.fp = FeaturePlan(sample_rate=44100)
self.fp.addFeature('feat: ')
self.engine = Engine()
self.engine.load(self.fp.getDataFlow())
self.afp = AudioFileProcessor()
self.mfcc = []
self.songs = []
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
class Extractor(object):
def __init__(self):
self.fp = FeaturePlan(sample_rate=44100)
self.fp.addFeature('feat: ')
self.engine = Engine()
self.engine.load(self.fp.getDataFlow())
self.afp = AudioFileProcessor()
self.mfcc = []
self.songs = []
def recurse(self, directory):
results = []
for root, dirs, files in os.walk(directory):
for f in files:
current_file = os.path.join(root, f)
if current_file.endswith('wav') and current_file not in results:
results.append(current_file)
print results
return results
def extract_feature(self, f):
self.afp.processFile(self.engine, f)
feats = self.engine.readAllOutputs()
return feats['feat']
def build_feature_space(self):
self.space = FeatureSpace('mfcc')
last_feature = None
for f in self.recurse(sys.argv[1]):
data = self.extract_feature(f)
temp_feature = Feature(f, ['mfcc', data])
self.space.add(temp_feature)
last_feature = temp_feature
print self.space.min_dist(last_feature)
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 _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 process_audio(audioFile, engine):
rename(audioFile,basename(audioFile))
afp = AudioFileProcessor()
afp.setOutputFormat('csv', config['OUTPUT_FOLDER'], {'Precision':'8'})
# afp.setOutputFormat('h5', OUTPUT_FOLDER_NAME, {'mode':'overwrite'})
afp.processFile(engine, basename(audioFile))
rename(basename(audioFile),audioFile)
return
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))
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
from pylab import plot, show
plot(frames)
show()
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'])
class Yaafe(EnhancedObject):
'''Yaafe toolbox wrapper. To be used with Database object.
Attributes:
sample_rate The Files' sample rate
plan_filename The Featue Plan filename
Methods:
process(audiofile) Process audiofile and return features
get_X(entries_list, feat) Fetch array of processed data from Database
get_y Fetch subdir i's from Database
`
Init:
Yaafe(sample_rate, feature_plan)
'''
_features = {
'spec_rolloff': ("SpectralRolloff blockSize=512 stepSize=128 "
"> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spec_shape': ("SpectralShapeStatistics blockSize=512 stepSize=128 "
"> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spec_flux': ("SpectralFlux blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'amp_mod': ("AmplitudeModulation blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'auto': ("AutoCorrelation blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'lpc': ("LPC blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'loudness': ("Loudness blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'mfcc': ("MFCC blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'mel_spectrum': ("MelSpectrum blockSize=512, stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'obsi': ("OBSI blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'obsir': ("OBSIR blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'perc_sharp': ("PerceptualSharpness blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'perc_spread': ("PerceptualSpread blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_crest': ("SpectralCrestFactorPerBand blockSize=512 "
"stepSize=128 > StatisticalIntegrator NbFrames=40 "
"StepNbFrames=8"),
'spec_decr': ("SpectralDecrease blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_flat': ("SpectralFlatness blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_flat_band': ("SpectralFlatnessPerBand blockSize=512 "
"stepSize=128 > StatisticalIntegrator NbFrames=40 "
"StepNbFrames=8"),
'spect_slope': ("SpectralSlope blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_var': ("SpectralVariation blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'temp_shape': ("TemporalShapeStatistics blockSize=512 stepSize=128 "
"> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'zcr': ("ZCR blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'env_shape': ("EnvelopeShapeStatistics blockSize=512 stepSize=128"
" > StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'comp_onest': ("ComplexDomainOnsetDetection blockSize=512 "
"stepSize=128 > StatisticalIntegrator NbFrames=40 "
"StepNbFrames=8"),
}
def __init__(self, sample_rate, features=None):
if features is None:
features = self._features
self.sample_rate = sample_rate
self.initialize(features)
self.features = features
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 save_fplan(self, name):
""" Save a feature plan (text file) """
text_file = open("{}.txt".format(name), 'w')
for name, desc in self.features.items():
text_file.write("{}: {}".format(name, desc))
text_file.close()
def process(self, audiofile):
""" Process function for running a file through yaafe's
feature extractor
"""
self.afp.processFile(self.engine, audiofile)
out = self.engine.readAllOutputs()
self.engine.flush()
return sorted(out)
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)
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)
class Yaafe(EnhancedObject):
'''Yaafe toolbox wrapper. To be used with Database object.
Attributes:
sample_rate The Files' sample rate
plan_filename The Featue Plan filename
Methods:
process(audiofile) Process audiofile and return features
get_X(entries_list, feat) Fetch array of processed data from Database
get_y Fetch subdir i's from Database
`
Init:
Yaafe(sample_rate, feature_plan)
'''
_features = {
'spec_rolloff': ("SpectralRolloff blockSize=512 stepSize=128 "
"> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spec_shape': ("SpectralShapeStatistics blockSize=512 stepSize=128 "
"> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spec_flux': ("SpectralFlux blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'amp_mod': ("AmplitudeModulation blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'auto': ("AutoCorrelation blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'lpc': ("LPC blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'loudness': ("Loudness blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'mfcc': ("MFCC blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'mel_spectrum': ("MelSpectrum blockSize=512, stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'obsi': ("OBSI blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'obsir': ("OBSIR blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'perc_sharp': ("PerceptualSharpness blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'perc_spread': ("PerceptualSpread blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_crest': ("SpectralCrestFactorPerBand blockSize=512 "
"stepSize=128 > StatisticalIntegrator NbFrames=40 "
"StepNbFrames=8"),
'spec_decr': ("SpectralDecrease blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_flat': ("SpectralFlatness blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_flat_band': ("SpectralFlatnessPerBand blockSize=512 "
"stepSize=128 > StatisticalIntegrator NbFrames=40 "
"StepNbFrames=8"),
'spect_slope': ("SpectralSlope blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'spect_var': ("SpectralVariation blockSize=512 stepSize=128 >"
"StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'temp_shape': ("TemporalShapeStatistics blockSize=512 stepSize=128 "
"> StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'zcr': ("ZCR blockSize=512 stepSize=128 > StatisticalIntegrator "
"NbFrames=40 StepNbFrames=8"),
'env_shape': ("EnvelopeShapeStatistics blockSize=512 stepSize=128"
" > StatisticalIntegrator NbFrames=40 StepNbFrames=8"),
'comp_onest': ("ComplexDomainOnsetDetection blockSize=512 "
"stepSize=128 > StatisticalIntegrator NbFrames=40 "
"StepNbFrames=8"),
}
def __init__(self, sample_rate, features=None):
if features is None:
features = self._features
self.sample_rate = sample_rate
self.initialize(features)
self.features = features
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 save_fplan(self, name):
""" Save a feature plan (text file) """
text_file = open("{}.txt".format(name), 'w')
for name, desc in self.features.items():
text_file.write("{}: {}".format(name, desc))
text_file.close()
def process(self, audiofile):
""" Process function for running a file through yaafe's
feature extractor
"""
self.afp.processFile(self.engine, audiofile)
out = self.engine.readAllOutputs()
self.engine.flush()
return sorted(out)
""" 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()
""" 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)