input: Text: Professor A: Am I on? I guess so. Radio two. Hmm. Radio two. Grad E: Hello? Professor A: Wow. Grad E: Mm - hmm. Hi? PhD B: Blow into it, it works really well. Grad F: Channel B. Professor A: People say the strangest things when their microphones are on. PhD D: Channel four. Test. PhD C: Uh - oh. PhD D: OK. PhD C: Radio four. Grad E: Hello? Professor A: So everybody everybody's on? PhD D: Today's Professor A: Yeah. So y you guys had a <unk>disfmarker<unk> a meeting with uh <unk>disfmarker<unk> with Hynek which I unfortunately had to miss. Um and uh somebody PhD C: Mmm. Professor A: eh e and uh I guess Chuck you weren't there either, so the uh PhD B: I was there. Professor A: Oh you were there? PhD B: With Hynek? Professor A: Yeah. PhD B: Yeah. Professor A: So everybody knows what happened except me. OK. <unk>vocalsound<unk> Maybe somebody should tell me. PhD C: Oh yeah. Alright. Well. Uh first we discussed about some of the points that I was addressing in the mail I sent last week. Professor A: Uh - huh. PhD C: So. Yeah. About the um, well <unk>disfmarker<unk> the downsampling problem. Professor A: Yeah. PhD C: Uh and about the f the length of the filters and <unk>disfmarker<unk> Yeah. Professor A: What was the <unk>disfmarker<unk> w what was the downsampling problem again? PhD C: So we had <unk>disfmarker<unk> Professor A: I forget. PhD C: So the fact that there <unk>disfmarker<unk> there is no uh low -
  Question: What did the professor think about clustering?
  Answer:
output: The professor was not too excited about clustering. He thought that the team could identify the bands that were the most promising and weigh them more highly in the model. As it stood, the model was using all bands with equal strength.