def calculate_features_per_band(self,
frequency_band,
also_one_band=False,
discard_bin_zero=False):
"""
:param frequency_band: FrequencyBand
:param also_one_band: boolean
:param discard_bin_zero: boolean
:return: list[FeatureTrack]
"""
flatness = feat_flat.Flatness()
energy = feat_energy.Energy()
flux = feat_flux.Flux()
centroid = feat_centroid.Centroid()
rolloff = feat_rolloff.Rolloff()
lowenergy = feat_lowenergy.LowEnergy()
bands = [b for b in frequency_band.bands()]
if also_one_band:
bands.append((int(frequency_band.low), int(frequency_band.high)))
for b in bands:
lowbin = self.spectrogram.freq_bin(b[0])
if lowbin == 0:
if discard_bin_zero:
lowbin = 1
highbin = self.spectrogram.freq_bin(b[1])
#print "calculating features for band in bin range: ", lowbin, highbin
features = []
flatness_feature = flatness.calc_track_band(
self.spectrogram, lowbin, highbin)
flatness_feature.metadata.feature += ("_" + str(b[0])) + (
"_" + str(b[1]))
features.append(flatness_feature)
energy_feature = energy.calc_track_band(self.spectrogram, lowbin,
highbin)
energy_feature.metadata.feature += ("_" + str(b[0])) + ("_" +
str(b[1]))
features.append(energy_feature)
flux_feature = flux.calc_track_band(self.spectrogram, lowbin,
highbin)
flux_feature.metadata.feature += ("_" + str(b[0])) + ("_" +
str(b[1]))
features.append(flux_feature)
centroid_feature = centroid.calc_track_band(
self.spectrogram, lowbin, highbin)
centroid_feature.metadata.feature += ("_" + str(b[0])) + (
"_" + str(b[1]))
features.append(centroid_feature)
rolloff_feature = rolloff.calc_track_band(self.spectrogram, lowbin,
highbin)
rolloff_feature.metadata.feature += ("_" + str(b[0])) + ("_" +
str(b[1]))
features.append(rolloff_feature)
lowenergy_feature = lowenergy.calc_track_band(
self.spectrogram, 10, lowbin, highbin)
lowenergy_feature.metadata.feature += ("_" + str(b[0])) + (
"_" + str(b[1]))
features.append(lowenergy_feature)
self.features_per_band = len(features)
self.band_features = np.hstack((self.band_features, features))
#MFCC hack
t = track.FeatureTrack()
t.data = mfcc.mfcc(self.spectrogram, 13)
t.metadata.sampling_configuration = self.spectrogram.metadata.sampling_configuration
feature = ""
for i in range(13):
feature = feature + "MFCC_" + str(i) + " "
t.metadata.feature = feature
t.metadata.filename = self.spectrogram.metadata.input.name
self.band_features = np.hstack((self.band_features, t))
#Zero crossings
t = track.FeatureTrack()
t.data = tdomf.zero_crossings(self.audio_data, 1024, 512)
t.metadata.sampling_configuration.fs = self.samplingrate
t.metadata.sampling_configuration.ofs = self.samplingrate / 1024
t.metadata.sampling_configuration.window_length = 512
t.metadata.feature = "TDZeroCrossings"
t.metadata.filename = self.spectrogram.metadata.input.name
self.band_features = np.hstack((self.band_features, t))
import scipy.io.wavfile
import matplotlib.pyplot as plt
import IPython.lib.display as display
import mir3.modules.tool.wav2spectrogram as spec
import mir3.modules.features.energy as energy
fname = 'audio/tabla.wav'
wav2spec = spec.Wav2Spectrogram(
) # Objeto que converte arquivos wav para espectrogramas
s = wav2spec.convert(open(fname, 'rb'),
window_length=1024,
window_step=512,
spectrum_type='magnitude')
en = energy.Energy()
f = en.calc_track(s)
T = f.metadata.sampling_configuration.ofs
t = np.linspace(0, len(f.data) / T, len(f.data))
plt.figure(figsize=(10, 6))
plt.plot(t, np.log10(f.data / np.max(f.data)))
plt.xlabel('Tempo (s)')
plt.ylabel('Energia (dB)')
plt.show()
display.Audio(fname)
import mir3.modules.features.flux as flux
wav2spec = spectrogram.Wav2Spectrogram() # Objeto que converte arquivos wav para espectrogramas
s = wav2spec.convert(open(fname, 'rb'), window_length=1024, window_step=512, spectrum_type='magnitude')
fness = flatness.Flatness()
f = fness.calc_track(s)
f1 = [np.average(f.data)]
flat_rock.append(f1)
centr = cent.Centroid()
centroid = centr.calc_track(s)
centroid1 = [np.average(centroid.data)]
cent_rock.append(centroid1)
# roff = roll.Rolloff()
# roll_off = roff.calc_track(s)
# roll_off1 = [np.average(roll_off)]
# rolloff_rock.append(roll_off1)
en = energ.Energy()
energy = en.calc_track(s)
energy1 = [np.average(energy.data)]
energy_rock.append(energy1)
fl = specfl.Flux()
flux = fl.calc_track(s)
flux1 = [np.average(flux.data)]
sflux_rock.append(flux1)
aux = [np.average(f.data)*np.average(centroid.data)*np.average(energy.data)*np.average(flux.data)]
cent_en_rock.append(aux)
flat_pop = [[]]
cent_pop = [[]]
rolloff_pop = [[]]
energy_pop = [[]]
sflux_pop = [[]]