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Author: theshredbox

BasicPythonScripts/Noise Reduction Filter/noise_reduction_filter.py

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+# feature extraction and preprocessing data
+#IMPORT THE LIBRARIES
+import librosa
+import librosa.display
+import pandas as pd
+import numpy as np
+import scipy.signal
+import matplotlib.pyplot as plt
+from PIL import Image
+from pathlib import Path
+from pylab import rcParams
+rcParams['figure.figsize'] = 14, 6
+
+import csv
+# Preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+#Reports
+from sklearn.metrics import classification_report, confusion_matrix
+
+import warnings
+warnings.filterwarnings('ignore')
+
+#READ THE AUDIO SAMPLES
+sr = 16000
+e_file1 = r'C:\Users\Aryan\PycharmProjects\Noise Reduction Filter\audio01.mp3'
+e_file2 = r'C:\Users\Aryan\PycharmProjects\Noise Reduction Filter\audio02.mp3'
+
+# 10 seconds of each file
+y1,sr = librosa.load(e_file1, mono=True, sr=sr, offset=0, duration=10)
+y2,sr = librosa.load(e_file2, mono=True, sr=sr, offset=0, duration=10)
+
+from IPython.display import Audio, IFrame, display
+
+display(Audio(y1,rate=sr))
+display(Audio(y2,rate=sr))
+
+#The audio samples used have high level background noises.
+librosa.display.waveplot(y1,sr=sr, color='g', x_axis='time');
+librosa.display.waveplot(y1,sr=sr, color='g', x_axis='time');
+
+#Logmel-spectogram
+#It is a very common preprocessing technique in audio detection applications is to transform audios to its log mel-spectogram representation
+
+S1 = librosa.feature.melspectrogram(y=y1, sr=sr, n_mels=64)
+D1 = librosa.power_to_db(S1, ref=np.max)
+librosa.display.specshow(D1, x_axis='time', y_axis='mel');
+
+S2 = librosa.feature.melspectrogram(y=y2, sr=sr, n_mels=64)
+D2 = librosa.power_to_db(S2, ref=np.max)
+librosa.display.specshow(D2, x_axis='time', y_axis='mel');
+
+#Filtering low-frequencies
+#A low-pass filter is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency.
+#The exact frequency response of the filter depends on the filter design.
+
+from scipy import signal
+import random
+
+
+def f_high(y,sr):
+    b,a = signal.butter(10, 2000/(sr/2), btype='highpass')
+    yf = signal.lfilter(b,a,y)
+    return yf
+
+yf1 = f_high(y1, sr)
+yf2 = f_high(y2, sr)
+
+librosa.display.waveplot(y1,sr=sr, colour='p', x_axis='time');
+librosa.display.waveplot(yf1,sr=sr, x_axis='time');
+
+librosa.display.waveplot(y2,sr=sr, x_axis='time');
+librosa.display.waveplot(yf2,sr=sr, x_axis='time');
+
+Sf1 = librosa.feature.melspectrogram(y=yf1, sr=sr, n_mels=64)
+Df1 = librosa.power_to_db(Sf1, ref=np.max)
+librosa.display.specshow(Df1, x_axis='time', y_axis='mel');
+Sf2 = librosa.feature.melspectrogram(y=yf2, sr=sr, n_mels=64)
+Df2 = librosa.power_to_db(Sf2, ref=np.max)
+librosa.display.specshow(Df2, x_axis='time', y_axis='mel');
+
+#Check the audio output
+display(Audio(yf1,rate=sr))
+display(Audio(yf2,rate=sr))
+
+#CONCLUSION
+#For both audio samples, the filter helped to isolate the interesting frequencies. The first audio is in a very good quality for distincting the birds.
+#The second audio still has some noise but significant improvements in noise reduction can be observed.

BasicPythonScripts/Noise Reduction Filter/requirements.txt

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+librosa==0.8.1
+pandas==1.3.5
+numpy==1.21.5
+scipy==1.9.0
+matplotlib==3.6.0
+pillow==7.2.1