enhancing the noise reduction using hamming window compared with blackman-harris window .

import numpy as np

import matplotlib.pyplot as plt

from scipy.signal import hamming, blackmanharris, convolve

from scipy.io import wavfile

# Load an example noisy audio signal (replace with your own .wav file)

# sample_rate, noisy_signal = wavfile.read('noisy_audio.wav')

# Generate a noisy signal as an example

fs = 1000 # Sample rate (Hz)

t = np.linspace(0, 1, fs) # Time vector (1 second)

clean_signal = np.sin(2 * np.pi * 50 * t) # Clean signal (sine wave of 50 Hz)

noise = np.random.normal(0, 0.5, fs) # Additive Gaussian noise

noisy_signal = clean_signal + noise # Noisy signal

# Apply Hamming Window

hamming_window = hamming(len(noisy_signal))

hamming_filtered_signal = convolve(noisy_signal, hamming_window, mode='same')

# Apply Blackman-Harris Window

blackmanharris_window = blackmanharris(len(noisy_signal))

blackmanharris_filtered_signal = convolve(noisy_signal, blackmanharris_window, mode='same')

# Plot the signals for comparison

plt.figure(figsize=(12, 8))

# Original noisy signal

plt.subplot(3, 1, 1)

plt.plot(t, noisy_signal, label='Noisy Signal', color='r')

plt.title('Noisy Signal')

plt.xlabel('Time [s]')

plt.ylabel('Amplitude')

plt.grid(True)

# Hamming window filtered signal

plt.subplot(3, 1, 2)

plt.plot(t, hamming_filtered_signal, label='Hamming Window Filtered', color='b')

plt.title('Hamming Window Filtered Signal')

plt.xlabel('Time [s]')

plt.ylabel('Amplitude')

plt.grid(True)

# Blackman-Harris window filtered signal

plt.subplot(3, 1, 3)

plt.plot(t, blackmanharris_filtered_signal, label='Blackman-Harris Window Filtered', color='g')

plt.title('Blackman-Harris Window Filtered Signal')

plt.xlabel('Time [s]')

plt.ylabel('Amplitude')

plt.grid(True)

# Show the plots

plt.tight_layout()

plt.show()

[William Rose]

What is the question? Convert python code to matlab?