Help on audio filter with FFT on python

Question

I need to take a .wav audio file that's noisy and filter out some noise. I have to do it using Fourier Transform. After some days researching and experimenting, I finally made a working function, the problem is that it doesn't work as I intend it to. Here is the function I made:

# Audio signal processing
from scipy.io.wavfile import read, write
import matplotlib.pyplot as plt
import numpy as np
from scipy.fft import fft, fftfreq, ifft


def AudioSignalProcessing(audio):

    
    # Import the .wav format audio into two variables: 
    # sampling (int)
    # audio signal (numpy array)
    
    sampling, signal = read(audio)
    
    # time duration of the audio
    length = signal.shape[0] / sampling

    # x axis based on the time duration
    time = np.linspace(0., length, signal.shape[0])
    
    # show original signal
    plt.plot(time, signal)
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")
    plt.title("Original signal")
    plt.show()

    
    # apply Fourier transform and normalize
    transform = fft(signal)
    
    # obtain frequencies
    xf = fftfreq(transform.size, 1/sampling) 
    
    # show transformed signal (frequencies domain)
    plt.plot(xf, abs(transform)/np.linalg.norm(transform))
    plt.xlabel("Frecuency (Hz)")
    plt.ylabel("Amplitude")
    plt.title("Frequency domain signal")
    plt.show()

    
    # filter the transformed signal to a 40% of its maximum amplitude
    threshold = np.amax(transform)*0.4
    filtered = np.copy(transform)
    filtered[abs(transform) < 0.4 * max(abs(transform))] = 0
    
    # show filtered transformed signal
    plt.plot(xf,abs(filtered)/np.linalg.norm(filtered))
    plt.xlabel("Frecuency (Hz)")
    plt.ylabel("Amplitude")
    plt.title("FILTERED time domain signal")
    plt.show()
    
    
    # transform the signal back to the time domain
    filtered = ifft(filtered)
    
    # show original signal filtered
    plt.plot(time, filtered)
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")
    plt.title("Filtered signal")
    plt.show()
    
    
    # convert audio signal to .wav format audio
    # write(audio.replace(".wav", " filtrado.wav"), sampling, filtrada.astype(signal.dtype))
    
    return None

AudioSignalProcessing("audio.wav")

The output plots are these:

When I listen to the filtered audio, it doesn't sound good. The voice that's talking seems like it's in another room.

I've read some related questions here and maybe it's not a code problem but a "theoretical" problem.

Answer 1

I've heard that some sounds in most languages use some very high and low pitched frequencies, so it would make sense it doesn't sound complete after those frequencies are removed. Getting rid of the noise without compromising heavily is probably more complicated than cropping the frequencies.