filters - Zero STFT bins (and not FFT bins)

Filtering by taking FFT, zeroing bins, and inverse FFT is a bad idea, as discussed here.

But what about:

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  take a STFT, (i.e. multiply the input signal by moving window function, and take FFT)

  
  


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  zero some bins in the STFT matrix

  
  


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  inverse STFT (using overlap-add)

  
  
  

?

I've tried it, it's not bad, but I've got horizontal ripples in the spectrogram:

from perso import stft
from scipy.io import wavfile
import numpy as np
sr, x = wavfile.read('in.wav')
s = stft.stft(x, fftsize = 4096)
s[:,0:11] = 0

s[:,13:] = 0
z = stft.istft(s)
z = np.float32(z)
wavfile.write('out.wav', sr, z)

Important note: why do I absolutely want to do the filter in the frequency domain? Because I want to do a filter that evolves over time... With a STFT, it would be super simple to make the filter evolve for each time frame...

for k in s.shape[0]:
    s[k,f(k):0] = 0       # where  f(k) varies over time (k) 

Is there a way to make a good-working STFT filtering in the frequency domain?