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In this way we can understand how to take better care of them, so they can serve us in a good way when needed. We can honour our tools by learning how they work. We should offer dignity to our tools the way angakkuit offered dignity to their tuurngait.

Maybe we are not the angakkuit of old, but we are clever people, and we have modern tools and theories that give great power to those of us who wield them. These spirits gave great power to those who wielded them, but they also had to be respected as they were still spirits. In days past, Inuit Angakkuit had tuurngait (spirit helpers) to assist them. Learning StyleДЄlways offer dignity to your spirit helpers. Signal processing is a core toolset used to record, save, edit, and play the media all around us. Without it, we wouldnвЂ™t have any of that: No cat videos. In a similar sense, signal processing is like a killer app: all the music, movies, and videos on your phones and other devices are made possible from signal processing theory. In the business of technology, the term вЂњkiller appвЂќ, refers to a computer application that everyone needs, wants, or canвЂ™t live without. This question was explored in Part One, but as this module goes deeper into the subject, it might be time to ask this question again. One can then communicate the idea of filtering low or high frequencies by separating and dropping only the basketball or tennis ball respectively.

Their combined bounce represents a rapid change from low to high (spatial) frequencies indicating an edge, as in rapidly going from dark to light or vice versa (rapid tonal contrast as edge). The basketball, having a lower bounce represents low frequencies, while the tennis ball with its higher bounce represents high frequencies. A non-computer activity to assist in explaining this relationship could be a physical demonstration followed by a discussion.ДЇor example, one could place a tennis ball on top of a basketball, let them drop, and discuss the combined bounce. Of these three, the concept most difficult to grasp is edge detection, which interprets edges as high spatial frequencies within an image.

How does the blur or sharpen features work in Photoshop or Gimp?ДЄs mentioned in the introduction, there are three major concepts demonstrated by Audacity, Sonic Visualiser, and Gimp.

How are artificial voices (such as AppleвЂ™s Siri or AmazonвЂ™s Alexa) synthesized?.

How do you read the frequencies that make up a signal?.

This module connects to elements of Math, Physics and Computer Studies. Furthermore, this notion teaches students to think critically about the tools, and that each has its own strengths and weaknesses based on the context of application. The narrative focus explores the major relationships and applications of these tools, alluding to but otherwise buffering the underlying math.ДЄ overarching theme is the idea that these are tools, and like any tool there is a period of learning and adjustment which can be initially intimidating, but these tools are still accessible. The main objective of this module is to introduce the core toolset of digital signal processing: Fourier TransformsВ and Filter Theory. Finally, Gimp explores spatial frequency and edge detection, resulting in various filters users can see and apply first hand. Sonic Visualiser explores spectrograms and the idea of data visualisation. In particular, three applications are demonstrated: Audacity, Sonic Visualiser, and Gimp.ДЄudacity explores how to read a basic spectrum plot, which in technical terms is a signal in the frequency or spectral domain. Part Two is intended to demonstrate basic, but important, audio and image editing applications, that we otherwise take for granted in our daily interactions with media. As stated in Part One, signal processing forms the foundation of the current multimedia revolution.

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