import librosa.display
import matplotlib.pyplot as plt
import numpy as np
from import_dataset import import_all_files
from group_data import get_data
from librosa.feature import mfcc
from librosa.feature import melspectrogram
from tqdm import tqdm
directory = "D:\\Google Drive\\Programs\\Jupyter\\Machine Learning\\project\\data\\audio_and_txt_files"
#%% Get clips
clips = import_all_files(directory)
#%% Get data and test separated only by class
data = get_data(clips, grouping="default", dtype="clip")
#%% Do mfcc on every clip
c = 1
images = [[], [], [], []]
for group in data:
for clip in tqdm(
group, "Taking MFCC of clips in group " + str(c) + " of " +
str(len(data))):
clip.mfcc = mfcc(y=clip.sound_data, sr=clip.sr)
c += 1
#%% Plot random mfccs from each group
c = 0
for group in data:
# Get images to plot
Created on Thu Jun 25 02:08:28 2020
@author: sukris
"""
from import_dataset import import_all_files
from group_data import get_data
import numpy as np
directory = "D:\\Google Drive\\Programs\\Jupyter\\Machine Learning\\project\\data\\audio_and_txt_files"
# Get clips
clips = import_all_files(directory)
# Get data and test separated only by class
data0 = get_data(clips, grouping="default", dtype="clip")
# Test if all clips in first group are normal (No wheezes or crackles)
for clip in data0[0]:
if clip.crackle or clip.wheeze:
print("Test failed, line 24",clip.crackle,clip.wheeze)
break
for clip in data0[1]:
if not (clip.crackle and not clip.wheeze):
print("Test failed, line 28",clip.crackle,clip.wheeze)
break
for clip in data0[2]:
if not (not clip.crackle and clip.wheeze):
print("Test failed, line 32",clip.crackle,clip.wheeze)
break
for clip in data0[3]: