Python audio signal doesn't filter well - numpy

Question

I need to take an .wav audio file that's noisy and filter out all that 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 = abs(fft(signal))
transform = transform/np.linalg.norm(transform)
# obtain frequencies
xf = fftfreq(transform.size, 1/sampling)
# show transformed signal (frequencies domain)
plt.plot(xf, 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 = transform[np.where(transform > threshold)]
xf_filtered = xf[np.where(transform > threshold)]
# show filtered transformed signal
plt.plot(xf_filtered, filtered)
plt.xlabel("Frecuency (Hz)")
plt.ylabel("Amplitude")
plt.title("FILTERED time domain signal")
plt.show()
# transform the signal back to the time domain
filtrada = ifft(signal)
# show original signal filtered
plt.plot(time, filtrada)
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")
Here is the output plots:
Original signal
Transformed signal
Filtered transformed signal
Filtered audio signal
The filtered frequencies don't look as I think they should, and after converting the filtered signal back to audio it doesn't sound good at all. Also, I've tried with different audios but the same filter distortion happens.

Answer 1

I suggest asking at https://dsp.stackexchange.com/ for detailed signal processing questions.
It looks like you want to keep only those frequency components that are within at least 40% of the maximum component. If that is the case:
Keep the complex form of the DFT, or you won't be able to transform back; so remove the abs from the line transform = abs(fft(signal)).
Don't use np.where to "keep" the frequencies you want; instead, set the places where the transform magnitude is below you threshold to 0; something like
transform[abs(transform) < 0.4 * max(abs(transform))] = 0
Finally, apply the inverse DFT to this altered transform; you've applied it to signal (see line filtrata = ifft(signal)). (You probably get warning when plotting filtrada about discarding imaginary values.)