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feature_class.py
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Tue, Sep 17, 18:09

feature_class.py
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import numpy as np
import librosa
from scipy import signal
from scipy.io import wavfile
from scipy.signal import butter,filtfilt
from scipy.stats import kurtosis
import scipy.signal as signal
from scipy.integrate import simps
# Class that contains the feature computation functions
class features:
# output should be a np.array
# names should be a list of the size of output
# add the number of features in output of each function
n_std_dev = 1
n_dummy = 2
n_EEPD = 19
n_PRE = 1
n_ZCR = 1
n_RMSP = 1
n_DF = 1
n_spectral_features = 6
n_SF_SSTD = 2
n_MFCC = 26
n_CF = 1
n_LGTH = 1
n_SSL_SD = 2
def __init__(self, FREQ_CUTS):
self.FREQ_CUTS = FREQ_CUTS # list of Frequency Bands for the PSD
self.n_PSD = len(FREQ_CUTS)
def std_dev(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = ['std_dev'] # list of output features
std_deviation = np.ones((1,1))*np.std(data[1])
return std_deviation, names
def dummy(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature (MUST BE AN ARRAY)
names = ['dummy_feature_2','dummy_3']
return np.array([1.,2.]), names
def fft(self,data):
"""
Compute the spectrum using FFT
"""
fs, cough = data
fftdata = np.fft.rfft(cough)
return fftdata
# Envelope Energy Peak Detection
def EEPD(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = []
fs,cough = data
fNyq = fs/2
nPeaks = []
freq_step = 50
for fcl in range(50,1000,freq_step):
names = names + ['EEPD'+str(fcl)+'_'+str(fcl+freq_step)]
fc = [fcl/fNyq, (fcl+50)/fNyq]
b, a = butter(1, fc, btype='bandpass')
bpFilt = filtfilt(b, a, cough)
b,a = butter(2, 10/fNyq, btype='lowpass')
eed = filtfilt(b, a, bpFilt**2)
eed = eed/np.max(eed+1e-17)
peaks,_ = signal.find_peaks(eed)
nPeaks.append(peaks.shape[0])
return np.array(nPeaks), names
# Phase Power Ratio Estimation
def PRE(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = ['Power_Ratio_Est']
fs,cough = data
phaseLen = int(cough.shape[0]//3)
P1 = cough[:phaseLen]
P2 = cough[phaseLen:2*phaseLen]
P3 = cough[2*phaseLen:]
f = np.fft.fftfreq(phaseLen, 1/fs)
P1 = np.abs(np.fft.fft(P1)[:phaseLen])
P2 = np.abs(np.fft.fft(P2)[:phaseLen])
P3 = np.abs(np.fft.fft(P3)[:phaseLen])
P2norm = P2/(np.sum(P1)+1e-17)
fBin = fs/(2*phaseLen +1e-17)
f750,f1k,f2k5 = int(-(-750//fBin)), int(-(-1000//fBin)), int(-(-2500//fBin))
ratio = np.sum(P2norm[f1k:f2k5]) / np.sum(P2norm[:f750])
return np.ones((1,1))*ratio, names
# Zero Crossing Rate
def ZCR(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = ['Zero_Crossing_Rate']
fs,cough = data
ZCR = (np.sum(np.multiply(cough[0:-1],cough[1:])<0)/(len(cough)-1))
return np.ones((1,1))*ZCR, names
# RMS Power
def RMSP(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = ['RMS_Power']
fs,cough = data
RMS = np.sqrt(np.mean(np.square(cough)))
return np.ones((1,1))*RMS, names
# Dominant Frequency
def DF(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = ['Dominant_Freq']
fs,cough = data
cough_fortan = np.asfortranarray(cough)
freqs, psd = signal.welch(cough_fortan)
DF = freqs[np.argmax(psd)]
return np.ones((1,1))*DF, names
def spectral_features(self, data):
names = ["Spectral_Centroid","Spectral_Rolloff","Spectral_Spread","Spectral_Skewness","Spectral_Kurtosis","Spectral_Bandwidth"]
fs, x = data
magnitudes = np.abs(np.fft.rfft(x)) # magnitudes of positive frequencies
length = len(x)
freqs = np.abs(np.fft.fftfreq(length, 1.0/fs)[:length//2+1]) # positive frequencies
sum_mag = np.sum(magnitudes)
# spectral centroid = weighted mean of frequencies wrt FFT value at each frequency
spec_centroid = np.sum(magnitudes*freqs) / sum_mag
#spectral roloff = frequency below which 95% of signal energy lies
cumsum_mag = np.cumsum(magnitudes)
spec_rolloff = np.min(np.where(cumsum_mag >= 0.95*sum_mag)[0])
#spectral spread = weighted standard deviation of frequencies wrt FFT value
spec_spread = np.sqrt(np.sum(((freqs-spec_centroid)**2)*magnitudes) / sum_mag)
#spectral skewness = distribution of the spectrum around its mean
spec_skewness = np.sum(((freqs-spec_centroid)**3)*magnitudes) / ((spec_spread**3)*sum_mag)
#spectral kurtosis = flatness of spectrum around its mean
spec_kurtosis = np.sum(((freqs-spec_centroid)**4)*magnitudes) / ((spec_spread**4)*sum_mag)
#spectral bandwidth = weighted spectral standard deviation
p=2
spec_bandwidth = (np.sum(magnitudes*(freqs-spec_centroid)**p))**(1/p)
return np.array([spec_centroid, spec_rolloff, spec_spread, spec_skewness, spec_kurtosis, spec_bandwidth]), names
# Spectral Flatness and spectral standard deviation
def SF_SSTD(self, data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of the feature
names = ['Spectral_Flatness', 'Spectral_StDev']
fs,sig = data
nperseg = min(900,len(sig))
noverlap = min(600,int(nperseg/2))
freqs, psd = signal.welch(sig, fs, nperseg=nperseg, noverlap=noverlap)
psd_len = len(psd)
gmean = np.exp((1/psd_len)*np.sum(np.log(psd + 1e-17)))
amean = (1/psd_len)*np.sum(psd)
SF = gmean/amean
SSTD = np.std(psd)
return np.array([SF, SSTD]), names
#Spectral Slope and Spectral Decrease
def SSL_SD(self,data):
names=['Spectral_Slope','Spectral_Decrease']
b1=0
b2=8000
Fs, x = data
s = np.absolute(np.fft.fft(x))
s = s[:s.shape[0]//2]
muS = np.mean(s)
f = np.linspace(0,Fs/2,s.shape[0])
muF = np.mean(f)
bidx = np.where(np.logical_and(b1 <= f, f <= b2))
slope = np.sum(((f-muF)*(s-muS))[bidx]) / np.sum((f[bidx]-muF)**2)
k = bidx[0][1:]
sb1 = s[bidx[0][0]]
decrease = np.sum((s[k]-sb1)/(f[k]-1+1e-17)) / (np.sum(s[k]) + 1e-17)
return np.array([slope, decrease]), names
#MFCC
def MFCC(self,data):
# data: wav file of segment; fs, signal = wavfile.read(file)
# output: value of MFCC coefficient
names = []; names_mean = []; names_std = []
fs, cough = data
n_mfcc = 13
for i in range(n_mfcc):
names_mean = names_mean + ['MFCC_mean'+str(i)]
names_std = names_std + ['MFCC_std'+str(i)]
names = names_mean + names_std
mfcc = librosa.feature.mfcc(y = cough, sr = fs, n_mfcc = n_mfcc)
mfcc_mean = mfcc.mean(axis=1)
mfcc_std = mfcc.std(axis=1)
mfcc = np.append(mfcc_mean,mfcc_std)
return mfcc, names
# Crest Factor
def CF(self,data):
"""
Compute the crest factor of the signal
"""
fs, cough = data
peak = np.amax(np.absolute(cough))
RMS = np.sqrt(np.mean(np.square(cough)))
return np.ones((1,1))*peak/RMS, ['Crest_Factor']
def LGTH(self,data):
"Compute the length of the segment in seconds"
fs, cough = data
return np.ones((1,1))*(len(cough)/fs), ['Cough_Length']
# Power spectral Density
def PSD(self,data):
feat = []
fs,sig = data
nperseg = min(900,len(sig))
noverlap=min(600,int(nperseg/2))
freqs, psd = signal.welch(sig, fs, nperseg=nperseg, noverlap=noverlap)
dx_freq = freqs[1]-freqs[0]
total_power = simps(psd, dx=dx_freq)
for lf, hf in self.FREQ_CUTS:
idx_band = np.logical_and(freqs >= lf, freqs <= hf)
band_power = simps(psd[idx_band], dx=dx_freq)
feat.append(band_power/total_power)
feat = np.array(feat)
feat_names = [f'PSD_{lf}-{hf}' for lf, hf in self.FREQ_CUTS]
return feat, feat_names

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