File size: 4,703 Bytes
586d5d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import parselmouth
from parselmouth.praat import call
import numpy as np
from scipy.stats import entropy
def extract_voice_features(audio_file):
sound = parselmouth.Sound(audio_file)
pitch = call(sound, "To Pitch", 0.0, 75, 600)
# Calculate jitter measures using individual Parselmouth functions
pointprocess = call(sound, "To PointProcess (periodic, cc)", 75, 600)
# Jitter measurements
jitter_percent = call(pointprocess, "Get jitter (local)", 0, 0, 0.0001, 0.02, 1.3)
jitter_abs = call(pointprocess, "Get jitter (local, absolute)", 0, 0, 0.0001, 0.02, 1.3)
jitter_rap = call(pointprocess, "Get jitter (rap)", 0, 0, 0.0001, 0.02, 1.3)
jitter_ppq5 = call(pointprocess, "Get jitter (ppq5)", 0, 0, 0.0001, 0.02, 1.3)
jitter_ddp = call(pointprocess, "Get jitter (ddp)", 0, 0, 0.0001, 0.02, 1.3)
# Shimmer measurements
shimmer = call([sound, pointprocess], "Get shimmer (local)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
shimmer_db = call([sound, pointprocess], "Get shimmer (local_dB)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
shimmer_apq3 = call([sound, pointprocess], "Get shimmer (apq3)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
shimmer_apq5 = call([sound, pointprocess], "Get shimmer (apq5)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
shimmer_apq11 = call([sound, pointprocess], "Get shimmer (apq11)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
shimmer_dda = call([sound, pointprocess], "Get shimmer (dda)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
# HNR (Harmonics-to-Noise Ratio)
harmonicity = call(sound, "To Harmonicity (cc)", 0.01, 75, 0.1, 1.0)
hnr = call(harmonicity, "Get mean", 0, 0)
# NHR (Noise-to-Harmonics Ratio) = 1 / linear_HNR
# HNR from Praat is in dB, so convert: linear_HNR = 10^(HNR_dB / 10)
# Guard against zero/negative linear values (unvoiced / very noisy signal)
if hnr > 0:
nhr_value = 1.0 / (10 ** (hnr / 10))
else:
nhr_value = float('inf')
# Get pitch periods for nonlinear features
# Alternative approach: use pitch values directly for period calculation
pitch_values = pitch.selected_array['frequency']
voiced_frames = pitch_values[pitch_values > 0] # Only voiced frames
if len(voiced_frames) > 0:
# Convert frequency to periods (1/frequency)
periods = 1.0 / voiced_frames
else:
periods = np.array([])
# Nonlinear features
if len(periods) < 50:
rpde = dfa = ppe = np.nan
else:
# PPE: Pitch Period Entropy
hist, _ = np.histogram(periods, bins=min(20, len(periods)//5), density=True)
hist = hist[hist > 0]
ppe = entropy(hist + 1e-10)
# Simplified RPDE: Recurrence Period Density Entropy
diffs = np.abs(np.diff(periods))
rec_threshold = np.std(diffs) * 0.1
rec_matrix = (np.abs(periods[:, None] - periods[None, :]) < rec_threshold).astype(float)
np.fill_diagonal(rec_matrix, 0)
hist_rpde, _ = np.histogram(rec_matrix.flatten(), bins=2, density=True)
rpde = entropy(hist_rpde + 1e-10)
# DFA: Detrended Fluctuation Analysis
y = np.cumsum(periods - np.mean(periods))
scales = np.logspace(np.log10(4), np.log10(len(y)/4), 8, dtype=int)
log_F = []
log_s = []
for s in scales:
if s > len(y) // 4:
continue
num_seg = len(y) // s
rms = []
for i in range(num_seg):
seg = y[i*s:(i+1)*s]
if len(seg) < 3:
continue
x = np.arange(len(seg))
p = np.polyfit(x, seg, 1)
detrend = seg - np.polyval(p, x)
rms.append(np.sqrt(np.mean(detrend**2)))
if rms:
F = np.sqrt(np.mean(rms))
log_F.append(np.log(F))
log_s.append(np.log(s))
if len(log_F) > 1:
slope, _ = np.polyfit(log_s, log_F, 1)
dfa = slope
else:
dfa = np.nan
return {
'Jitter(%)': jitter_percent * 100,
'Jitter(Abs)': jitter_abs,
'Jitter:RAP': jitter_rap,
'Jitter:PPQ5': jitter_ppq5,
'Jitter:DDP': jitter_ddp,
'Shimmer': shimmer,
'Shimmer(dB)': shimmer_db,
'Shimmer:APQ3': shimmer_apq3,
'Shimmer:APQ5': shimmer_apq5,
'Shimmer:APQ11': shimmer_apq11,
'Shimmer:DDA': shimmer_dda,
'NHR': nhr_value,
'HNR': hnr,
'RPDE': float(rpde),
'DFA': float(dfa),
'PPE': float(ppe)
}
# Load audio file
# audio_file = "test_voice.wav"
# features = measure_jitter_shimmer(audio_file)
# print(features) |