Jehoshaphat I. Abu
std::steam

std::steam

100 Days Of ML Code — Day 068

100 Days Of ML Code — Day 068

Jehoshaphat I. Abu's photo
Jehoshaphat I. Abu
·Sep 15, 2018·

2 min read

Recap From Day 067

Day 067, we looked at how Gesture Variation Follower works. You can catch up using the link below. 100 Days Of ML Code — Day 067 Recap From Day 066medium.com

Today, we’ll continue from where we left off in day 067

Working with time

How Gesture Variation Follower works continued.

What we saw yesterday was the iteration on one particular combination of estimated values used to compute the weight for the estimation. So the process is iterated over each one of the hundreds of combinations of potential values. In order to give a probability distribution over the elements to estimate such as the speed or the size of the recognized gesture.

[Source](https://cdn.hashnode.com/res/hashnode/image/upload/v1632826783861/Si7XGYK0j.html)Source

The whole process will then be repeated for each new observation. However before considering a new observation, what we want is not hundreds of combinations together with their weights, that is to say, their probabilities, but only one value that gives us the recognized gesture and the estimated alignment, speed and size. To do that, we have several possibilities. Let’s see two of those.

The first possibility is to take the combination of values that has the highest weight, which means the highest probability. Although conceptually acceptable, in practice this approach is very noisy and gives jumpy estimations.

[Source](https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists-v/sessions/working-with-time)Source

The second approach is to take the weighted mean. The weighted mean is computed by summing up the value of an element multiplied by its weight. This approach is much more robust and is actually the one implemented in GVF. When the process is repeated for a new observation, the hundreds of combinations of estimated valuation are updated from the previous estimation and then the process is repeated.

That’s all for day 068. I hope you found this informative. Thank you for taking time out of your schedule and allowing me to be your guide on this journey. And until next time, be legendary.

References

*https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists-v/sessions/working-with-time*

 
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