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R10770/src/DSP.py
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DSP.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.signal import cwt
from scipy.signal import hilbert
from scipy.signal import resample
from scipy.signal import decimate
from scipy.signal import spectrogram
from scipy.signal.windows import get_window
import os
from feature_class import features
def classify_cough(x, fs, model, scaler):
"""Classify whether an inputted signal is a cough or not using filtering, feature extraction, and ML classification
Inputs:
x: (float array) raw cough signal
fs: (int) sampling rate of raw signal
model: cough classification ML model loaded from file
Outputs:
result: (float) probability that a given file is a cough
"""
try:
x,fs = preprocess_cough(x,fs)
data = (fs,x)
FREQ_CUTS = [(0,200),(300,425),(500,650),(950,1150),(1400,1800),(2300,2400),(2850,2950),(3800,3900)]
features_fct_list = ['EEPD','ZCR','RMSP','DF','spectral_features','SF_SSTD','SSL_SD','MFCC','CF','LGTH','PSD']
feature_values_vec = []
obj = features(FREQ_CUTS)
for feature in features_fct_list:
feature_values, feature_names = getattr(obj,feature)(data)
for value in feature_values:
if isinstance(value,np.ndarray):
feature_values_vec.append(value[0])
else:
feature_values_vec.append(value)
feature_values_scaled = scaler.transform(np.array(feature_values_vec).reshape(1,-1))
result = model.predict_proba(feature_values_scaled)[:,1]
return result[0]
except:
"Feature extraction fails when the audio is completely silent"
return 0
def preprocess_cough(x,fs, cutoff = 6000, normalize = True, filter_ = True, downsample = True):
"""
Normalize, lowpass filter, and downsample cough samples in a given data folder
Inputs: x*: (float array) time series cough signal
fs*: (int) sampling frequency of the cough signal in Hz
cutoff: (int) cutoff frequency of lowpass filter
normalize: (bool) normailzation on or off
filter: (bool) filtering on or off
downsample: (bool) downsampling on or off
*: mandatory input
Outputs: x: (float32 array) new preprocessed cough signal
fs: (int) new sampling frequency
"""
fs_downsample = cutoff*2
#Preprocess Data
if len(x.shape)>1:
x = np.mean(x,axis=1) # Convert to mono
if normalize:
x = x/(np.max(np.abs(x))+1e-17) # Norm to range between -1 to 1
if filter_:
b, a = butter(4, fs_downsample/fs, btype='lowpass') # 4th order butter lowpass filter
x = filtfilt(b, a, x)
if downsample:
x = signal.decimate(x, int(fs/fs_downsample)) # Downsample for anti-aliasing
fs_new = fs_downsample
return np.float32(x), fs_new
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