def plot_pitches(filename, pitches, confidences, tolerance=0.8, hop_s=(512 // 1), samplerate=(0 // 1)):
skip = 1
pitches = array(pitches[skip:])
confidences = array(confidences[skip:])
times = [t * hop_s for t in range(len(pitches))]
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1 = get_waveform_plot(filename, samplerate=samplerate, block_size=hop_s, ax=ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.set_xlabel('')
ax2 = fig.add_subplot(312, sharex=ax1)
ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'
if os.path.isfile(ground_truth):
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:, 2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:, 0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis(ymin=0.9 * true_freqs.min(), ymax=1.1 * true_freqs.max())
# plot raw pitches
# ax2.plot(times, pitches, '.-')
# plot cleaned up pitches
cleaned_pitches = pitches
cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)
cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
ax2.plot(times, cleaned_pitches, 'b.')
ax2.axis(ymin=0.9 * cleaned_pitches.min(), ymax=1.1 * cleaned_pitches.max())
# ax2.axis( ymin = 55, ymax = 70 )
plt.setp(ax2.get_xticklabels(), visible=False)
ax2.set_ylabel('f0 (midi)')
# plot confidence
ax3 = fig.add_subplot(313, sharex=ax1)
# plot the confidence
ax3.plot(times, confidences)
# draw a line at tolerance
ax3.plot(times, [tolerance] * len(confidences))
ax3.axis(xmin=times[0], xmax=times[-1])
ax3.set_ylabel('confidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
savefig(filename + 'fig.png')
plt.show()
# do plotting
from numpy import arange
from demo_waveform_plot import get_waveform_plot
from demo_waveform_plot import set_xlabels_sample2time
import matplotlib.pyplot as plt
fig = plt.figure()
plt.rc("lines", linewidth=".8")
wave = plt.axes([0.1, 0.75, 0.8, 0.19])
get_waveform_plot(source_filename, samplerate, block_size=hop_s, ax=wave)
wave.xaxis.set_visible(False)
wave.yaxis.set_visible(False)
all_times = arange(mfccs.shape[0]) * hop_s
n_coeffs = mfccs.shape[1]
for i in range(n_coeffs):
ax = plt.axes([0.1, 0.75 - ((i + 1) * 0.65 / n_coeffs), 0.8, 0.65 / n_coeffs], sharex=wave)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.plot(all_times, mfccs.T[i])
# add time to the last axis
set_xlabels_sample2time(ax, frames_read, samplerate)
# plt.ylabel('spectral descriptor value')
ax.xaxis.set_visible(True)
wave.set_title("MFCC for %s" % source_filename)
plt.show()
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:, 2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:, 0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis(ymin=0.9 * true_freqs.min(), ymax=1.1 * true_freqs.max())
# plot raw pitches
ax2.plot(times, pitches, '.g')
# plot cleaned up pitches
cleaned_pitches = pitches
#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)
#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
ax2.plot(times, cleaned_pitches, '.-')
#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )
#ax2.axis( ymin = 55, ymax = 70 )
plt.setp(ax2.get_xticklabels(), visible=False)
ax2.set_ylabel('f0 (midi)')
# plot confidence
ax3 = fig.add_subplot(313, sharex=ax1)
# plot the confidence
ax3.plot(times, confidences)
# draw a line at tolerance
ax3.plot(times, [tolerance] * len(confidences))
ax3.axis(xmin=times[0], xmax=times[-1])
ax3.set_ylabel('confidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
plt.show()
#plt.savefig(os.path.basename(filename) + '.svg')
if read < hop_s: break
if 1:
print "done computing, now plotting"
import matplotlib.pyplot as plt
from demo_waveform_plot import get_waveform_plot
from demo_waveform_plot import set_xlabels_sample2time
fig = plt.figure()
plt.rc('lines',linewidth='.8')
wave = plt.axes([0.1, 0.75, 0.8, 0.19])
get_waveform_plot(filename, samplerate, block_size = hop_s, ax = wave )
wave.yaxis.set_visible(False)
wave.xaxis.set_visible(False)
n_plots = len(energies.T)
all_desc_times = [ x * hop_s for x in range(len(energies)) ]
for i, band in enumerate(energies.T):
ax = plt.axes ( [0.1, 0.75 - ((i+1) * 0.65 / n_plots), 0.8, 0.65 / n_plots], sharex = wave )
ax.plot(all_desc_times, band, '-', label = 'band %d' % i)
#ax.set_ylabel(method, rotation = 0)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.axis(xmax = all_desc_times[-1], xmin = all_desc_times[0])
ax.annotate('band %d' % i, xy=(-10, 10), xycoords='axes points',
horizontalalignment='right', verticalalignment='bottom',
)
set_xlabels_sample2time( ax, all_desc_times[-1], samplerate)
#plt.ylabel('spectral descriptor value')
ax.xaxis.set_visible(True)
plt.show()
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:,2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:,0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() )
# plot raw pitches
ax2.plot(times, pitches, '.g')
# plot cleaned up pitches
cleaned_pitches = pitches
#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)
#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
ax2.plot(times, cleaned_pitches, '.-')
#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )
#ax2.axis( ymin = 55, ymax = 70 )
plt.setp(ax2.get_xticklabels(), visible = False)
ax2.set_ylabel('f0 (midi)')
# plot confidence
ax3 = fig.add_subplot(313, sharex = ax1)
# plot the confidence
ax3.plot(times, confidences)
# draw a line at tolerance
ax3.plot(times, [tolerance]*len(confidences))
ax3.axis( xmin = times[0], xmax = times[-1])
ax3.set_ylabel('confidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
plt.show()
#plt.savefig(os.path.basename(filename) + '.svg')
def pitch_track(filename, samplerate, Display=False):
Display_Plot = Display
from aubio import source, pitch, freqtomidi
#########VARY this Value
tolerance = 0.85
silence_threshold = 0.00004
#####################
# if len(sys.argv) < 2:
# print "Usage: %s <filename> [samplerate]" % sys.argv[0]
# sys.exit(1)
#filename = sys.argv[1]
downsample = 1
samplerate = 44100 / downsample
if len( sys.argv ) > 2: samplerate = int(sys.argv[2])
win_s = 4096 / downsample # fft size
hop_s = 512 / downsample # hop size
s = source(filename, samplerate, hop_s)
samplerate = s.samplerate
# may be able to use onset tracking for silence threshold information
pitch_o = pitch("yinfft", win_s, hop_s, samplerate)
pitch_o.set_unit("midi")
pitch_o.set_tolerance(tolerance)
pitches = []
confidences = []
#****************************************************
# total number of frames read
total_frames = 0
while True:
samples, read = s()
pitch = pitch_o(samples)[0]
#pitch = int(round(pitch))
confidence = pitch_o.get_confidence()
#if confidence < 0.8: pitch = 0.
#print "%f %f %f" % (total_frames / float(samplerate), pitch, confidence)
pitches += [pitch]
confidences += [confidence]
total_frames += read
if read < hop_s: break
if 0: sys.exit(0)
####use librosa to get samples###
signal, sr = librosa.load(filename, sr = 44100)
signal2 = []
start = 0
for i in range(512, len(signal), hop_s):
end = i
signal2.append(np.average(np.square(signal[start:end])))
start = i
uplist = []
dlist = []
for i in range( len(signal2)-2):
if signal2[i+2] - signal2[i] > 0.1*signal2[i]:
uplist.append(i)
dlist.append(signal2[i+2]-signal2[i])
##GET PITCHES CONFIDENCES AND TIMES##
skip = 1
pitches = array(pitches[skip:])
confidences = array(confidences[skip:])
times = [t * hop_s for t in range(len(pitches))]
#**************************************
ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'
if os.path.isfile(ground_truth):
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:,2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:,0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() )
cleaned_pitches = pitches
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
octave_cleaned = octave_error(cleaned_pitches)
for i in range(1):
octave_cleaned = octave_error3(octave_cleaned)
octave_cleaned = silence_mask(octave_cleaned, signal2, silence_threshold)
octave_cleaned = octave_error(octave_cleaned)
#*****************************************************
# load file and rms
#trumpet,_ = librosa.load(filename,sr=44100)
rms = librosa.feature.rmse(y=signal)
#create a velocity reference********************************************
# Get peak rms value for file to set as reference for velocity = 100
midi_vel_ref = []
sig_len = int(len(signal)/1024.)
for si in xrange(0,sig_len - 1):
start = si * 1024
stop = start + 1024
midi_vel_ref = np.append(midi_vel_ref,rms_db(signal[start:stop]))
midi_vel_ref = np.amax(midi_vel_ref)
x_rms = rms_db(signal) #this is going to be the frames for the midi note
#**********************************
###standalone onset detection
#****************************************
onsets_clean, onsets_pos = onset_detect(rms, Display = False)
onsets_clean = np.array(onsets_clean, dtype = int)
midi_out_new2, midi_out_new =midi_output(onsets_clean, octave_cleaned)
#print midi_out_new2, onsets_clean, midi_out_new
for i in range(len(midi_out_new2)):
start = midi_out_new2[i][1]
stop = midi_out_new2[i][2]
midi_vel = midi_velocity(signal[start*512:stop*512], midi_vel_ref) #this is going to neeed the frames in signal
midi_out_new2[i].append(midi_vel)
#print results
#*********************************************
output = np.zeros((len(midi_out_new2), len(midi_out_new2[0])))
for i in range(len(output)):
for j in range(len(output[0])):
output[i,j] = midi_out_new2[i][j]
output = np.rint(output)
output = np.array(output, dtype = int)
if Display_Plot:
####PLOTTING######
#****************************************************
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1 = get_waveform_plot(filename, samplerate = samplerate, block_size = hop_s, ax = ax1)
plt.setp(ax1.get_xticklabels(), visible = False)
ax1.set_xlabel('')
#plot cleaned pitches
ax2 = fig.add_subplot(312, sharex = ax1)
ax2.plot(times, pitches, '.g')
ax2.plot(times, cleaned_pitches, '.-')
ax2.plot(times, octave_cleaned, '.r' )
plt.setp(ax2.get_xticklabels(), visible = False)
ax2.set_ylabel('f0 (midi)')
ax2.vlines(onsets_clean * 512,0,120,color='k',linestyle='--',linewidth=1)
##### plot confidence
ax3 = fig.add_subplot(313, sharex = ax1)
ax3.plot(times, confidences)
ax3.plot(times, [tolerance]*len(confidences)) # draw a line at tolerance
ax3.axis( xmin = times[0], xmax = times[-1])
ax3.set_ylabel('condidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
print len(output)
print output
plt.show(block=False)
return output, len(signal)
if 1:
print("done computing, now plotting")
import matplotlib.pyplot as plt
from demo_waveform_plot import get_waveform_plot
from demo_waveform_plot import set_xlabels_sample2time
fig = plt.figure()
plt.rc('lines',linewidth='.8')
wave = plt.axes([0.1, 0.75, 0.8, 0.19])
get_waveform_plot(filename, samplerate, block_size = hop_s, ax = wave )
wave.yaxis.set_visible(False)
wave.xaxis.set_visible(False)
all_desc_times = [ x * hop_s for x in range(len(all_descs["default"])) ]
n_methods = len(methods)
for i, method in enumerate(methods):
#ax = fig.add_subplot (n_methods, 1, i)
#plt2 = plt.axes([0.1, 0.1, 0.8, 0.65], sharex = plt1)
ax = plt.axes ( [0.1, 0.75 - ((i+1) * 0.65 / n_methods), 0.8, 0.65 / n_methods], sharex = wave )
ax.plot(all_desc_times, all_descs[method], '-', label = method)
#ax.set_ylabel(method, rotation = 0)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.axis(xmax = all_desc_times[-1], xmin = all_desc_times[0])
ax.annotate(method, xy=(-10, 0), xycoords='axes points',
horizontalalignment='right', verticalalignment='bottom',
)
set_xlabels_sample2time(ax, all_desc_times[-1], samplerate)
#plt.ylabel('spectral descriptor value')
ax.xaxis.set_visible(True)
plt.show()
from numpy import arange
from demo_waveform_plot import get_waveform_plot
from demo_waveform_plot import set_xlabels_sample2time
import matplotlib.pyplot as plt
fig = plt.figure()
plt.rc('lines', linewidth='.8')
wave = plt.axes([0.1, 0.75, 0.8, 0.19])
get_waveform_plot(source_filename, samplerate, block_size=hop_s, ax=wave)
wave.xaxis.set_visible(False)
wave.yaxis.set_visible(False)
all_times = arange(mfccs.shape[0]) * hop_s
n_coeffs = mfccs.shape[1]
for i in range(n_coeffs):
ax = plt.axes(
[0.1, 0.75 - ((i + 1) * 0.65 / n_coeffs), 0.8, 0.65 / n_coeffs],
sharex=wave)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.plot(all_times, mfccs.T[i])
# add time to the last axis
set_xlabels_sample2time(ax, frames_read, samplerate)
#plt.ylabel('spectral descriptor value')
ax.xaxis.set_visible(True)
wave.set_title('MFCC for %s' % source_filename)
plt.show()
def audio_analysis(filename):
print type(filename)
# filename = file_name
downsample = 1
samplerate = 44100 / downsample
win_s = 4096 / downsample # fft size
hop_s = 512 / downsample # hop size
s = source(filename, samplerate, hop_s)
samplerate = s.samplerate
tolerance = 0.8
pitch_o = pitch("yin", win_s, hop_s, samplerate)
pitch_o.set_unit("freq")
pitch_o.set_tolerance(tolerance)
pitches = []
confidences = []
time_stamp = []
# total number of frames read
total_frames = 0
while True:
samples, read = s()
pitch = pitch_o(samples)[0]
#pitch = int(round(pitch))
confidence = pitch_o.get_confidence()
#if confidence < 0.8: pitch = 0.
print "%f %f %f" % (total_frames / float(samplerate), pitch,
confidence)
time_stamp += [(total_frames / float(samplerate))]
pitches += [pitch]
confidences += [confidence]
total_frames += read
if read < hop_s: break
if 0: sys.exit(0)
#print time_stamp
# Invoking aubiocut to detect when a word of spoken.
sub = subprocess.Popen(['python', 'aubiocut', filename],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
out = sub.communicate()[0]
# Importing Regular Expression Modules for extracting the output[timestamp] of Aubiocut
import re
timestamps = re.findall("\d+.\d+\d+\d+\d+", out)
print timestamps
extracted_voice = []
import math
for i in timestamps:
i = float(i)
for j in range(len(time_stamp)):
#Using the floor functions the timestamp is extracted when speakers spoke a word.
temp1 = math.floor(i * 10) / 10
temp2 = math.floor((time_stamp[j]) * 10) / 10
#print str(temp1)+ " and "+str(temp2)
# if pitch >10000 then it is considered Noice in our environment.
if temp1 == temp2 and pitches[j] < 10000.0:
#print "True"+str(j)+pitches[j]
extracted_voice += [pitches[j]]
print extracted_voice
avg = 0.0
for i in extracted_voice:
avg += i
avg = avg / (len(extracted_voice))
print "Average Pitch of Extracted Voice: " + str(avg)
gender = clf.classify([avg])
#print pitches
from numpy import array, ma
import matplotlib.pyplot as plt
from demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time
skip = 1
pitches = array(pitches[skip:])
confidences = array(confidences[skip:])
times = [t * hop_s for t in range(len(pitches))]
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1 = get_waveform_plot(filename,
samplerate=samplerate,
block_size=hop_s,
ax=ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.set_xlabel('')
def array_from_text_file(filename, dtype='float'):
import os.path
from numpy import array
filename = os.path.join(os.path.dirname(__file__), filename)
return array([line.split() for line in open(filename).readlines()],
dtype=dtype)
ax2 = fig.add_subplot(312, sharex=ax1)
import sys, os.path
ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'
if os.path.isfile(ground_truth):
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:, 2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:, 0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis(ymin=0.9 * true_freqs.min(), ymax=1.1 * true_freqs.max())
# plot raw pitches
ax2.plot(times, pitches, '--g')
# plot cleaned up pitches
cleaned_pitches = pitches
#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)
#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
ax2.plot(times, cleaned_pitches, '.-')
#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )
#ax2.axis( ymin = 55, ymax = 70 )
plt.setp(ax2.get_xticklabels(), visible=False)
ax2.set_ylabel('f0 (Hz)')
# plot confidence
ax3 = fig.add_subplot(313, sharex=ax1)
# plot the confidence
ax3.plot(times, confidences)
# draw a line at tolerance
ax3.plot(times, [tolerance] * len(confidences))
ax3.axis(xmin=times[0], xmax=times[-1])
ax3.set_ylabel('condidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
plt.show()
#plt.savefig(os.path.basename(filename) + '.svg')
return gender
def get_pitch(filename):
from aubio import source, pitch
downsample = 1
samplerate = 44100 // downsample
win_s = 4096 // downsample # fft size
hop_s = 512 // downsample # hop size
s = source(filename, samplerate, hop_s)
samplerate = s.samplerate
tolerance = 0.8
pitch_o = pitch("yin", win_s, hop_s, samplerate)
pitch_o.set_unit("midi")
pitch_o.set_tolerance(tolerance)
pitches = []
confidences = []
# total number of frames read
total_frames = 0
while True:
samples, read = s()
pitch = pitch_o(samples)[0]
#pitch = int(round(pitch))
confidence = pitch_o.get_confidence()
#if confidence < 0.8: pitch = 0.
#print("%f %f %f" % (total_frames / float(samplerate), pitch, confidence))
pitches += [pitch]
confidences += [confidence]
total_frames += read
if read < hop_s: break
if 0: sys.exit(0)
#print pitches
import os.path
from numpy import array, ma
import matplotlib.pyplot as plt
from demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time
skip = 1
pitches = array(pitches[skip:])
confidences = array(confidences[skip:])
times = [t * hop_s for t in range(len(pitches))]
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1 = get_waveform_plot(filename,
samplerate=samplerate,
block_size=hop_s,
ax=ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.set_xlabel('')
def array_from_text_file(filename, dtype='float'):
filename = os.path.join(os.path.dirname(__file__), filename)
return array([line.split() for line in open(filename).readlines()],
dtype=dtype)
ax2 = fig.add_subplot(312, sharex=ax1)
ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'
if os.path.isfile(ground_truth):
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:, 2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:, 0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis(ymin=0.9 * true_freqs.min(), ymax=1.1 * true_freqs.max())
ax2.plot(times, pitches, '.g')
cleaned_pitches = pitches
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
ax2.plot(times, cleaned_pitches, '.-')
plt.setp(ax2.get_xticklabels(), visible=False)
ax2.set_ylabel('f0 (midi)')
ax3 = fig.add_subplot(313, sharex=ax1)
ax3.plot(times, confidences)
ax3.plot(times, [tolerance] * len(confidences))
ax3.axis(xmin=times[0], xmax=times[-1])
ax3.set_ylabel('condidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
# plt.show()
plt.savefig(filename.replace('.mp3', '.pdf'))
significant = confidences > tolerance
return array(
times)[significant], pitches[significant], confidences[significant]
def audio_analysis (filename):
print type(filename)
# filename = file_name
downsample = 1
samplerate = 44100 / downsample
win_s = 4096 / downsample # fft size
hop_s = 512 / downsample # hop size
s = source(filename, samplerate, hop_s)
samplerate = s.samplerate
tolerance = 0.8
pitch_o = pitch("yin", win_s, hop_s, samplerate)
pitch_o.set_unit("freq")
pitch_o.set_tolerance(tolerance)
pitches = []
confidences = []
time_stamp=[]
# total number of frames read
total_frames = 0
while True:
samples, read = s()
pitch = pitch_o(samples)[0]
#pitch = int(round(pitch))
confidence = pitch_o.get_confidence()
#if confidence < 0.8: pitch = 0.
print "%f %f %f" % (total_frames / float(samplerate), pitch, confidence)
time_stamp+=[(total_frames/float(samplerate))]
pitches += [pitch]
confidences += [confidence]
total_frames += read
if read < hop_s: break
if 0: sys.exit(0)
#print time_stamp
# Invoking aubiocut to detect when a word of spoken.
sub = subprocess.Popen(['python', 'aubiocut', filename], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
out = sub.communicate()[0]
# Importing Regular Expression Modules for extracting the output[timestamp] of Aubiocut
import re
timestamps=re.findall("\d+.\d+\d+\d+\d+", out)
print timestamps
extracted_voice=[]
import math
for i in timestamps:
i=float(i)
for j in range(len(time_stamp)):
#Using the floor functions the timestamp is extracted when speakers spoke a word.
temp1=math.floor(i*10)/10
temp2=math.floor((time_stamp[j])*10)/10
#print str(temp1)+ " and "+str(temp2)
# if pitch >10000 then it is considered Noice in our environment.
if temp1==temp2 and pitches[j]<10000.0:
#print "True"+str(j)+pitches[j]
extracted_voice+=[pitches[j]]
print extracted_voice
avg=0.0
for i in extracted_voice:
avg+=i
avg=avg/(len(extracted_voice))
print "Average Pitch of Extracted Voice: "+ str(avg)
gender=clf.classify([avg])
#print pitches
from numpy import array, ma
import matplotlib.pyplot as plt
from demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time
skip = 1
pitches = array(pitches[skip:])
confidences = array(confidences[skip:])
times = [t * hop_s for t in range(len(pitches))]
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1 = get_waveform_plot(filename, samplerate = samplerate, block_size = hop_s, ax = ax1)
plt.setp(ax1.get_xticklabels(), visible = False)
ax1.set_xlabel('')
def array_from_text_file(filename, dtype = 'float'):
import os.path
from numpy import array
filename = os.path.join(os.path.dirname(__file__), filename)
return array([line.split() for line in open(filename).readlines()],
dtype = dtype)
ax2 = fig.add_subplot(312, sharex = ax1)
import sys, os.path
ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'
if os.path.isfile(ground_truth):
ground_truth = array_from_text_file(ground_truth)
true_freqs = ground_truth[:,2]
true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
true_times = float(samplerate) * ground_truth[:,0]
ax2.plot(true_times, true_freqs, 'r')
ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() )
# plot raw pitches
ax2.plot(times, pitches, '--g')
# plot cleaned up pitches
cleaned_pitches = pitches
#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)
#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)
cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
ax2.plot(times, cleaned_pitches, '.-')
#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )
#ax2.axis( ymin = 55, ymax = 70 )
plt.setp(ax2.get_xticklabels(), visible = False)
ax2.set_ylabel('f0 (Hz)')
# plot confidence
ax3 = fig.add_subplot(313, sharex = ax1)
# plot the confidence
ax3.plot(times, confidences)
# draw a line at tolerance
ax3.plot(times, [tolerance]*len(confidences))
ax3.axis( xmin = times[0], xmax = times[-1])
ax3.set_ylabel('condidence')
set_xlabels_sample2time(ax3, times[-1], samplerate)
plt.show()
#plt.savefig(os.path.basename(filename) + '.svg')
return gender
