Recap From Day 049

In day 049, we started looking at working with time, dynamic time warping. We saw that Dynamic time warping is a method that can be used to compute the similarity between two sequence of data over time. In general, DTW is a method that calculates an optimal match between two given sequences (e.g. time series) with certain restriction and rules.

Today, we’ll look at what we can do when we have a good measure of similarity between one sequence and another.

Working with time

Dynamic Time Warping

What can we do when we have a good measure of similarity between one sequence and another. First of all, we can look at the sequence of features between some time in the past and the time right now and when we can compare it to a sequence that we recorded previously.

Source

With dynamic time warping we can detect when a specific gesture occurs even when we’re not using explicit segmentation with a button or something equivalent. Dynamic time warping will spot the fact we’ve just completed the gesture. In fact, this is sometimes called gesture spotting.

Dynamic time warping can be used to do classification using nearest-neighbor. We can use the measure of similarity between two gestures gotten from dynamic time warping as a distance metric within a nearest neighbor classifier. But instead of comparing two points in feature space using Euclidean distance, we can compare two sequences of features using their distance according to dynamic time warping.

Up until now, we’ve been seeing that dynamic time warping computes the similarity between two sequences but it computes the distance. We could say that two sequence are maximally similar when their distance is zero and the greater the distance the less similar they are.

Source

Certainly, this notion of distance works well inside of a nearest neighbor classifier.

You deserve some accolades for being here till day 050. 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.

Reference

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

*https://en.wikipedia.org/wiki/Dynamic_time_warping*