Adding a method to check background noise remove

LectureSummarizingApp/noiseRemove.py

+import numpy as np
+import scipy as sp
+from scipy.io.wavfile import read
+from scipy.io.wavfile import write
+from scipy import signal
+import matplotlib.pyplot as plt
+#get_ipython().magic('matplotlib inline')
+
+(Frequency, array) = read('lectures/Lecture01.wav')
+
+len(array)
+
+plt.plot(array)
+plt.title('Original Signal Spectrum')
+plt.xlabel('Frequency(Hz)')
+plt.ylabel('Amplitude')
+
+FourierTransformation = sp.fft(array)
+
+scale = sp.linspace(0, Frequency, len(array))
+
+plt.stem(scale[0:5000], np.abs(FourierTransformation[0:5000]), 'r')
+plt.title('Signal spectrum after FFT')
+plt.xlabel('Frequency(Hz)')
+plt.ylabel('Amplitude')
+
+
+GuassianNoise = np.random.rand(len(FourierTransformation))
+
+
+NewSound = GuassianNoise + array
+
+write("New-Sound-Added-With-Guassian-Noise.wav", Frequency, NewSound)
+
+b,a = signal.butter(5, 1000/(Frequency/2), btype='highpass')
+
+filteredSignal = signal.lfilter(b,a,NewSound)
+plt.plot(filteredSignal) # plotting the signal.
+plt.title('Highpass Filter')
+plt.xlabel('Frequency(Hz)')
+plt.ylabel('Amplitude')
+
+c,d = signal.butter(5, 380/(Frequency/2), btype='lowpass') # ButterWorth low-filter
+newFilteredSignal = signal.lfilter(c,d,filteredSignal) # Applying the filter to the signal
+plt.plot(newFilteredSignal) # plotting the signal.
+plt.title('Lowpass Filter')
+plt.xlabel('Frequency(Hz)')
+plt.ylabel('Amplitude')
+
+write("file.wav", Frequency, np.int16(newFilteredSignal/np.max(np.abs(newFilteredSignal)) * 32767))
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