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R13251/Visualization Code/Utils_librosa.py
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Utils_librosa.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jul 4 07:06:49 2024
@author: Vigneashwara.P
email: vigneashwara.pandiyan@tii.ae
_status_: "Prototyping"
_maintainer_ = Vigneashwara Pandiyan
Modification and reuse of this code should be authorized by the first owner, code author(s)
"""
import pandas as pd
import glob
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib import cm
import ntpath
import re
from scipy import signal
# import pywt
import seaborn as sns
from sklearn import preprocessing
import os
import librosa
# MEL(data2,sample_rate,time,folder_created,lowpass=750000)
def MEL(data_new,sample_rate,time,path,lowpass):
plt.rcParams.update(plt.rcParamsDefault)
vmin = 95 # minimum dB value for the color range
vmax = 140 # maximum dB value for the color range
fig, ax = plt.subplots(figsize=(12, 9),nrows=4, ncols=1, sharex=True)
D = librosa.amplitude_to_db(np.abs(librosa.stft(data_new)), ref=np.min)
img = librosa.display.specshow(D, y_axis='linear', x_axis='time',vmin=vmin, vmax=vmax,
sr=sample_rate, ax=ax[0])
ax[0].set(title=f"Linear-frequency power spectrogram")
ax[0].label_outer()
fig.colorbar(img, ax= ax[0], format='%+2.0f dB')
hop_length = 1024
D = librosa.amplitude_to_db(np.abs(librosa.stft(data_new, hop_length=hop_length)),
ref=np.min)
librosa.display.specshow(D, y_axis='log', sr=sample_rate, hop_length=hop_length,vmin=vmin, vmax=vmax,
x_axis='time', ax=ax[1])
ax[1].set(title=f"Log-frequency power spectrogram")
ax[1].label_outer()
fig.colorbar(img, ax= ax[1], format='%+2.0f dB')
vmin = 0 # minimum dB value for the color range
vmax = 40 # maximum dB value for the color range
# fig, ax = plt.subplots()
S = librosa.feature.melspectrogram(y=data_new, sr=sample_rate)
S_dB = librosa.power_to_db(S)
img = librosa.display.specshow(S_dB, x_axis='time', y_axis='mel', sr=sample_rate,vmin=vmin, vmax=vmax,
fmax=sample_rate, ax=ax[2])
ax[2].set(title=f"Mel-Spectrogram")
ax[2].label_outer()
fig.colorbar(img, ax= ax[2], format='%+2.0f dB')
S = librosa.feature.mfcc(y=data_new, sr=sample_rate)
S_dB = librosa.power_to_db(S)
img = librosa.display.specshow(S_dB, x_axis='time', y_axis='mel', sr=sample_rate,vmin=vmin, vmax=vmax,
fmax=sample_rate, ax=ax[3])
ax[3].set(title=f"MFCC")
ax[3].label_outer()
fig.colorbar(img, ax= ax[3], format='%+2.0f dB')
graphname='Librosa'+'.png'
plt.savefig(os.path.join(path, graphname), bbox_inches='tight',dpi=100)
plt.show()
plt.clf()
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