Recommendation for courses / studies on digital signal processing
I've kind of grouped your subjects into larger overall subjects.
Note that there's a lot of overlap here, with the possible exception of actually making it work in a microprocessor (except -- in my opinion the best person to implement something is someone who understands it.  So -- overlap).
Specifically, you could claim that it's all applied math.  Or all signal processing.  You could even stretch this list a bit and wedge it into control theory, but it wouldn't be an easy fit.

Applied Math

Singular Value Decomposition
Principal Component Analysis
Machine Learning for Audio Processing


Signal Processing

Digital Filter Design
Kalman Filters (especially in the context of inertia measurement units and nonlinear problems)
Wavelet Transforms


Control Theory

Kalman Filters (especially in the context of inertia measurement units and nonlinear problems)
Control theory (implementing a control algorithm and tuning it for a problem)


Embedded Software (or systems) Engineering

Running a control algorithm on a microprocessor



A weakness of the US tertiary educational system is that all of these subjects will be used in engineering departments, but they're all really applied math.  So different universities will group them differently, and it can be hard to find a program that will cover them all.  I'm not sure if that applies to where you live.  I happened to end up -- by luck more than design -- at an institution that let me get enough of a grounding in the related subjects that I could learn the rest on my own (Worcester Polytechnic Institute -- it's a great place).
I would suggest that you approach this by finding a university program that includes the classes you need to get a grounding in this, and get your second master's.  Don't expect to learn all of this within a Master's program -- I can lay claim to about 90% of this list, but about half of it are things that I learned on the job, over the last three decades.
Classes to look for are below.  Note that you're not going to have time for them all, so you'll need to pick and choose.  If you want to grow into your full list, take as much math as you can and expect to do self-study:

Stochastic signals and systems (basically, this is the study of finding the optimal system to process a signal with random components).

If you haven't taken it, a 4th-year course on statistics from the math department.


Real analysis (AKA &quot;Advanced Calculus&quot;, but I think that's an obsolete name).  It's an invaluable help when you're sitting at your desk at work wondering if the nifty new thing you're applying is actually mathematically sound.
If it's offered, estimation and detection theory.  This is basically a follow-on to stochastic signals and systems.  It's a deep dive into the math underlying the detection of signals, signals in noise, etc.  The one that I took had you derive the basic Kalman filter from first principles in a homework problem -- which gives you an idea of the depth of the subject.
Any class on optimal state estimation (i.e., Kalman filtering and all its variations).
Signal processing, assuming you haven't taken a class in it.
Digital signal processing, ditto.
Control theory classes will just be called &quot;control theory&quot;, but you may not be able to fit much in.  If there's a class offered in state-space systems and you've already got classical (transfer function) control under your belt -- take it.
If you want to specialize in control theory, go for nonlinear control or whatever is offered.  But if you do that, you probably won't have time for the Detection &amp; Estimation class, so think hard.

I'm not sure what to suggest on the implementation front -- that's basically yet another specialty.  I just picked that up on my own, on the way.  But, I started out as a kit with a hobby in electronics and a microprocessor board, so I was already doing basic embedded programming when I was 13.  Then when I got an EE degree, any class that involved digital circuits was either &quot;oh, yes, I've done this&quot;, or &quot;oooh!  what a neat formal way to do what I've struggled through intuitively&quot;.