Jehoshaphat I. Abu
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std::steam

100 Days Of ML Code — Day 069

100 Days Of ML Code — Day 069

Jehoshaphat I. Abu's photo
Jehoshaphat I. Abu
·Sep 16, 2018·

2 min read

Recap From Day 068

Day 068, we looked at how Gesture Variation Follower works. You can catch up using the link below. 100 Days Of ML Code — Day 068 Recap From Day 067medium.com

Today, we’ll look at sonic interaction with GVF.

Working with time

Sonic interaction with GVF

Behind the scene, GVF uses a set of parameters in order to perform the inference that we saw on day 067. Beyond the role in the algorithm machinery, these parameters have a direct influence on the method behaviour, and so on the resulting interaction. This can be useful if we know that the performed gesture will be very close to the template, and only slight variations could occur.

For instance, an expert musician’s gesture is known to be very consistent. In the case, we would like to recognize musician gestures and tracks subtle variations, such configuration of GVF is the one to consider. On the other hand, high adaptation values will allow huge variations in gestures to be tracked but will lead to a less good precision in the estimation.

So there is a trade-off between the precision and speed of adaptation, that highly depends on the use cases considered. And it’s up to the designer or the artist to configure the algorithm for this desired behaviour. To finish with GVF, let’s see how we can use such a tool to control sound.

This GVF based musical instrument will be based on drawn gesture shapes to make it simpler. Each gesture shape will be associated with a sound, and each continuous variation will control some synthesis parameters.

That’s all for day 069. 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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