So the best way to begin explaining wavelets is through analogy to music. (I’m cheating a bit since this explanation is alluded to in the article 😆)
It is a nontrivial practical fact that you can express any reasonable sound as a sum of sine waves. Yes, by combining enough sine waves (which individually “move” for all time) in just the right weights, you can come up with “any” sound you want. And then, it turns out that if you give me just the weights, I can give you back the sound itself. And as a final physical fact, it turns out that we hear the weights of any given sound, averaged over some finite window of time (more on this window in a minute). Hence why we can pick out instruments from a band. And lastly, some phenomenon are easier to analyze by looking at the weights; music is an excellent example. In fact, when I mix music in my rapidly diminishing free time, I am often staring at a graph of the weights and seeing this these weights add together and make the instruments work together.
Formally, we use one of the Fourier transform frameworks. Each weight is associated to one unique sine with a given frequency. The size of the weight is called the frequency response at that frequency.
Now for many, many purposes, breaking up a signal in terms of sines is a perfectly appropriate choice. However, what you lose when you choose to look at just the weights is all timing information. (This is why I included the detail about the window in how you hear stuff. If you heard all frequencies over all time with no window, you would not be able to perceive rhythm.) The solution in music often is to simply impose a window on the signal and slide it as the play head moves.
However, we must now leave the realm of music to talk about wavelets in a domain where they are typically used. Now imagine you want to apply all your intuition about music [more accurately, theory of sound, not music theory] to seismic signals. Well… unfortunately, we really do care about the timing of these signals. So instead of ditching all the magical techniques of linear algebra and transform analysis, we can pick a new set of waves and decompose in terms of those. I.e., we use a transform “midway” between the Fourier transform and the identity transform (doing nothing, just working with the raw signal).
One way to do this is to start with a wavelet: any waveform with zero average and finite “length”. Then, you take this mother wavelet, and you create child wavelets by stretching and/or shifting the mother wavelet. Then, you break up your signals in terms of the wavelets. (I think you pick wavelets based on what you want to find. For example, if you want to find sharp changes, you can pick a Haar wavelet, which is basically a family of rectangles. And then, you can pick wavelets based on their statistics so that the variances and higher order statistics vanish.)
My favorite book on Wavelets, and one of my personal favorite books, is A Wavelet Tour of Signal Processing: The Sparse Way by Mallat. It’s a bit mathematically challenging, but it’s such a fun read. One of the few books I actually own in print. And it’s one those cool fields in math where you basically just start with like pure math and end up with some incredibly practical results and algorithms.
Research:
My background is in control theory. I work on analyzing dynamical systems, specifically large-scale, complicated (typically people use the word “complex”, but I really mean complicated, because all the systems I work on evolve in real spaces) systems that evolve in time according to differential equations (e.g. electronic circuits, mechanical systems, power systems) or difference equations (e.g. sampled versions of the above). The goal of my research is to make just enough assumptions and prove it using calculus so future generations don’t have to do so much calculus…because you have to do so much calculus that not even a supercomputer can solve it.
PM me for more details since I’m not quite ready to dox myself 😆
PM_ME_VINTAGE_30S@lemmy.sdf.org 11 hours ago
Okay here are the 🫘:
Wavelets:
So the best way to begin explaining wavelets is through analogy to music. (I’m cheating a bit since this explanation is alluded to in the article 😆)
It is a nontrivial practical fact that you can express any reasonable sound as a sum of sine waves. Yes, by combining enough sine waves (which individually “move” for all time) in just the right weights, you can come up with “any” sound you want. And then, it turns out that if you give me just the weights, I can give you back the sound itself. And as a final physical fact, it turns out that we hear the weights of any given sound, averaged over some finite window of time (more on this window in a minute). Hence why we can pick out instruments from a band. And lastly, some phenomenon are easier to analyze by looking at the weights; music is an excellent example. In fact, when I mix music in my rapidly diminishing free time, I am often staring at a graph of the weights and seeing this these weights add together and make the instruments work together.
Formally, we use one of the Fourier transform frameworks. Each weight is associated to one unique sine with a given frequency. The size of the weight is called the frequency response at that frequency.
Now for many, many purposes, breaking up a signal in terms of sines is a perfectly appropriate choice. However, what you lose when you choose to look at just the weights is all timing information. (This is why I included the detail about the window in how you hear stuff. If you heard all frequencies over all time with no window, you would not be able to perceive rhythm.) The solution in music often is to simply impose a window on the signal and slide it as the play head moves.
However, we must now leave the realm of music to talk about wavelets in a domain where they are typically used. Now imagine you want to apply all your intuition about music [more accurately, theory of sound, not music theory] to seismic signals. Well… unfortunately, we really do care about the timing of these signals. So instead of ditching all the magical techniques of linear algebra and transform analysis, we can pick a new set of waves and decompose in terms of those. I.e., we use a transform “midway” between the Fourier transform and the identity transform (doing nothing, just working with the raw signal).
One way to do this is to start with a wavelet: any waveform with zero average and finite “length”. Then, you take this mother wavelet, and you create child wavelets by stretching and/or shifting the mother wavelet. Then, you break up your signals in terms of the wavelets. (I think you pick wavelets based on what you want to find. For example, if you want to find sharp changes, you can pick a Haar wavelet, which is basically a family of rectangles. And then, you can pick wavelets based on their statistics so that the variances and higher order statistics vanish.)
My favorite book on Wavelets, and one of my personal favorite books, is A Wavelet Tour of Signal Processing: The Sparse Way by Mallat. It’s a bit mathematically challenging, but it’s such a fun read. One of the few books I actually own in print. And it’s one those cool fields in math where you basically just start with like pure math and end up with some incredibly practical results and algorithms.
Research:
My background is in control theory. I work on analyzing dynamical systems, specifically large-scale, complicated (typically people use the word “complex”, but I really mean complicated, because all the systems I work on evolve in real spaces) systems that evolve in time according to differential equations (e.g. electronic circuits, mechanical systems, power systems) or difference equations (e.g. sampled versions of the above). The goal of my research is to make just enough assumptions and prove it using calculus so future generations don’t have to do so much calculus…because you have to do so much calculus that not even a supercomputer can solve it.
PM me for more details since I’m not quite ready to dox myself 😆
redsand@lemmy.dbzer0.com 8 hours ago
Another book added to the list… I may never get to the Silmarillion. War and Peace certain is never happening.
Good luck with your research. May you find more answers than questions.