Jehoshaphat I. Abu
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100 Days Of ML Code — Day 064

100 Days Of ML Code — Day 064

Jehoshaphat I. Abu's photo
Jehoshaphat I. Abu
·Sep 11, 2018·

2 min read

Recap From Day 063

Day 063, we looked at Working With Time, Gesture Follower(GF.). We saw that Gesture Follower, is able to align the incoming sequence onto the template and compute their similarity on the fly. Which means at each new incoming feature value that is to say, while we’re performing the gesture. 100 Days Of ML Code — Day 063 Recap From Day 062medium.com

Today, we’ll look at how gesture follower works

Working with time

How gesture follower works

…Indeed, If GF is like DTW, but real time, why dynamic time warping wouldn’t work the same way? The answer is that GF is probably stick while DTW is not. GF consider that the input gesture can vary from the recorded template in amplitude and time. These variations, to simplify, are considered as noise in the data. And our model, by a mean, often zero, which means that the expecting noise value is zero. And variance, which is the tolerance to variations. Hence, variations are modeled as Gaussian distributions. More precisely, GF as a notion of tolerance in terms of amplitude between the recorded gesture and the ones performed in real time. This means, for instance, that if we recorded a circle, and then we try to perform more or less the same circle, if we perform it slightly smaller, GF will be tolerant and says, “Ok this is still the same circle.” This is properly handled in the model by a Gaussian noise for the observation.

[Source](https://cdn.hashnode.com/res/hashnode/image/upload/v1632826831422/dfGVklE13.html)Source

Second, GF has a model of time, and a motion of tolerance in time. Taking the same example, if we now try to perform the same circle again, but we do it slightly faster, GF will understand it as the same circle but it will be able to see that we passed through the same values than the recording circle. For instance, bottom position, then left, the top, and finally right. But faster than in the recorded gesture. The tolerance in time will help GF to be more flexible in time and to handle lost data.

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

But the approach has several limitations since tolerance in time and amplitudes are considered as noise in the performed gestures, GF will not be able to consider variations as deliberate expressive variations. In other words, GF does not have a model of expressive variations and this brings limitations.

That’s all for day 064. 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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