michelin_chalupa
michelin_chalupa t1_iykvrqx wrote
Isn’t it common knowledge that deep learning is usually not the best solution for modeling time series?
michelin_chalupa t1_iui244a wrote
Reply to comment by quichemiata in [D] How do I get certain number of frames before every point-of-impact in a tennis game video? by ChaosAdm
Yep.
michelin_chalupa t1_iuesch3 wrote
Reply to comment by dual_carriageway in [D] How do I get certain number of frames before every point-of-impact in a tennis game video? by ChaosAdm
Building off this: could annotate a hundred or so impacts, fine tune an audio embedding, and do a similarity search over the video audio.
Could work pretty well, since players grunting would probably be pretty distinct from other audio.
michelin_chalupa t1_iuekl9p wrote
Reply to [D] How do I get certain number of frames before every point-of-impact in a tennis game video? by ChaosAdm
The simple way would be to just annotate those impact frames. A more sophisticated way might involve tracking the ball, and annotating those frames where it’s estimated velocity is low (which will of course be noisy, depending on the angle of the camera wrt it’s trajectory).
If it were me, I’d just hunker down for an afternoon and manually annotate those impact frames.
michelin_chalupa t1_iynu2rx wrote
Reply to [D] Do neural networks take care of feature engineering? by Steve_Sizzou
Some of those additional features are going to be correlated, or otherwise implicit. I think the most basic thing you’d want to do is some smoothing.
Maybe even try DFT representations, depending on how your signals behave (not familiar with EEG data myself).