input: Text: PhD A: Alright. We're on. Professor B: Test, um. Test, test, test. Guess that's me. Yeah. OK. Grad D: Ooh, Thursday. Professor B: So. There's two sheets of paper in front of us. PhD A: What are these? PhD E: Yeah. So. Professor B: This is the arm wrestling? PhD C: Uh. Yeah, we formed a coalition actually. PhD E: Yeah. Almost. PhD C: We already made it into one. Professor B: Oh, good. PhD C: Yeah. Professor B: Excellent. PhD E: Yeah. Professor B: That's the best thing. PhD E: Mm - hmm. Professor B: So, tell me about it. PhD E: So it's <unk>disfmarker<unk> well, it's <unk>pause<unk> spectral subtraction or Wiener filtering, um, depending on if we put <unk>disfmarker<unk> if we square the transfer function or not. Professor B: Right. PhD E: And then with over - estimation of the noise, depending on the, uh <unk>disfmarker<unk> the SNR, with smoothing along time, um, smoothing along frequency. Professor B: Mm - hmm. PhD E: It's very simple, smoothing things. Professor B: Mm - hmm. PhD E: And, um, <unk>vocalsound<unk> the best result is <unk>vocalsound<unk> when we apply this procedure on FFT bins, uh, with a Wiener filter. Professor B: Mm - hmm. PhD E: And there is no noise addition after <unk>disfmarker<unk> after that. Professor B: OK. PhD E: So it's good because <unk>vocalsound<unk> <unk>vocalsound<unk> it's difficult when we have to add noise to
  Question: What did the professor think about improving the model?
  Answer:
output: The professor wanted to know how much the model improved due to frame dropping. He thought four or five changes would result in good improvements. The professor highlighted that improvements should not come at a higher bandwidth.