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
  Question: Summarize the discussion on frame dropping and noise estimation
  Answer:
output: The team thought that the differences in performance between well-matched and high mismatch may have to do with the frame dropping problem. Tinkering around and changing a few small things was suggested as a way of improving performance. The team though it would also be nice to have the net on the server side where it would use less bandwidth. The team also discussed if averaging over the entire spectrum was a good idea.