r/DSP

I recently got into a master’s program in ECE, but my background is mainly in computer science and mathematics. To prepare, I’ve been self-studying Signals and Systems.

At this point, I can conceptually understand the major topics: discrete-time and continuous-time convolution, Fourier series and Fourier transforms, Laplace transforms, Z-transforms, sampling, and related ideas.

However, my understanding is still mostly conceptual and surface level. I can handle basic plug-and-chug problems, but I struggle with more rigorous or unfamiliar problems that require deeper understanding.

I’m planning to take DSP in my first semester, and I’m trying to decide how to prepare.

Should I spend more time doing a rigorous review of Signals and Systems before starting DSP, especially by working through harder problems?

Or would it be better to jump into DSP and review the necessary Signals and Systems material as it comes up?

I’d appreciate advice from people who have taken graduate-level DSP, especially those who came from a CS or non-EE background.

Hey everyone,

I’m in my 4th year of studies (honour's where I'm from) and looking into image processing / CV as a career, but I’m more interested in the job side than the theory. I haven't gotten any real-world experience, so I'd like to find out what others thought.

What is the job market like right now (in your specific region)? What kinds of roles are common (CV engineer, DSP engineer, ML vision, embedded vision, etc.) and which industries hire most people in this space (big tech, defence, medical, automotive, robotics, etc.)?

Also curious about job quality overall — pay, workload, stability, and growth. Is it mostly research roles or more applied engineering work in practice? And is the field becoming fully deep learning-based, or is there still a lot of traditional DSP/image processing in industry?

Finally, how hard is it to break into compared to general software engineering or ML roles, and is it a good long-term career bet or too niche?

Would appreciate any real-world insight.

Thanks everyone!

Just shipped MAGDA 0.8.0 and thought people here might find the DSP side interesting.

MAGDA is an open source cross-platform DAW (JUCE + Tracktion Engine) and this release is heavily built around FAUST. Almost the entire FX bank is now written in FAUST and compiled to native C++ at build time.

The new filter device alone includes SVF, Ladder, Korg35, Oberheim SEM, Sallen-Key and diode ladder models. Reverbs, modulation FX, delays, dynamics, pitch FX, etc are now FAUST-based too.

There’s also a Faust device inside the DAW where users can write .dsp directly in-app, or generate FAUST code from a natural language prompt through the AI panel. Right now this path uses the interpreter (not JIT yet), so CPU usage is much higher than the build-time compiled devices.

Project: https://github.com/Conceptual-Machines/magda-core
Download: https://magda.land
FAUST: https://faust.grame.fr/

Happy to answer questions about the integration/build pipeline if anyone’s interested.

Hey all very new to this subreddit…Quick background. I come from a pretty standard conservatory musician lifestyle, but moved to SF to understand how tech interfaces with music. As a result I’ve also found an affinity of music copyright, content recognition, and metadata- now working in a music tech startup up as low level legal admin role. Working with technical people such as the data, software, and ML eng gave me an insight a world that i did not know about…Music information retrieval and fell in love with the concepts - and it all ties to my niche realm of humanities, musician ship and copyright law- especially with rise of AI.

That said last year I was fortunate enough to get into Georgia Institute of Technology (MS Music Technology) and NYU (MM Music Technology) with a plan to be in research groups relating to music cognition perception, audio content analysis/Music Info Reterival, and finally for my own personal practice creative technology sectors interfacing with music- say sound design, application design, systems designs. I had to let go of NYU..they were stubborn. But Ga tech allowed me to defer due to cost…and agreed to let me attend part time if i could find a full time remote or atl based role. now a year later my situation hasn’t gotten better- My job is REFUSING to let me move to Atlanta (im hybrid…even though my whole team is nYC) and no luck finding additional roles. I still can’t afford to go..which is heart breaking. Being SF i see the changes happening in all sectors and now I feel stuck..im giving myself a month to decide- do i take out loans or just let the opportunity go and see what else can be done…

Im reaching out to see if you all have any insight on what to do next if you were in my position? Are there alternative education route..should i just give up? TBH For the roles I want i don’t see any other alternative way to break into to the realm of real technical music technology that changes the world- think Dolby, apple, some streaming services that utilize music in everyday life…even med tech. They all seem to require advanced degrees…and very specific technical knowledge or at least to be able to speak the language…

Oh also I have undergrad loans..a lot..my family is/was not wealthy. Some people said maybe consider Europe..but is that any better? Particularly for my niche field.

Thanks all getting pretty desperate and sad here..i work incredibly hard to essentially pivot (albeit i have an advantage of the conservatory music background)

Are there any books that teach DSP for people with a mathematics background?

I am really struggling to follow and understand DSP. It seems that it's taught in the most obtuse and confusing way possible on purpose.

In mathematics you always formally define every concept in a rigorous and formal manner. For example a isomorphism, it's just an invertible bijection. This definition holds regardless in any context it appears. You might generalize it or add additional constraints to get new morphisms but the underlying concept is the same. The good mathbooks always introduce a concept by first motivating it, defining it, stating the theorem and then proving it and giving examples.

In DSP words and concepts appear out of the blue and barely anything is formally defined. For example, the lector used the concept of "pole" out of the blue. I dig and search online and see that they are the solutions for of the polynomials in a transfer function which in the z domain. Now I am sitting here wondering wtf does any of these mean and how is it related to filters.

Hi, BS/MS in EE with professional exp in radar systems and ML. I am hoping to apply to some PhD programs in the fall and for the next 1.5 years Id like to spend time doing research. Any academics need some help? i am willing to work for free / volunteer because i am mainly hoping to gain experience, mentorship, and a LoR. I am a US citizen.

Hello! Tomorrow is the last day for choosing masters in my EE degree. I am interested in DSP because I like math more than physics, but from what I hear there the field has become saturated and does not have that many jobs anymore. I am also considering choosing the communication engineering track, but shouldn't I choose the RF masters in that case? Are there need for communication engineers that are not actually specialized in RF? I live in Sweden btw.

See the links with courses for the two masters bellow:

Information and Network engineering
https://www.kth.se/en/studies/master/information-and-network-engineering/courses-information-network-engineering-1.673889

RF

https://www.kth.se/en/studies/master/electromagnetics-fusion-and-space-engineering/courses-electromagnetics-fusion-space-engineering-1.268257

This was an effect called the 'hyper resonator' in the old AX300G guitar multi fx from Korg.

Does anyone have any ideas how it could be approximated using DSP? It's classed under modulation, and i can only assume it's some kind of envelope-triggered resonant filter.

alternatively, to avoid re-inventing the wheel, does anyone know of a plugin that does something similar? that would be good to know also.

demo of the effect is from 2:28 - 3:00 in the video

1.0 SUBSTRATE

Parameter	Specification
Material	Schott Borofloat 33 borosilicate glass
Dimensions	25.00 mm × 25.00 mm × 6.000 mm ±0.025 mm
Flatness	<2 μm across full surface
Surface finish	<5 nm Ra, both faces
Edge chamfer	0.20 mm × 45°, no chips >50 μm
Quantity	3 identical substrates

2.0 GRID LAYOUT

Parameter	Specification
Grid type	4×4 Cartesian
Pitch	5.000 mm ±0.010 mm center-to-center
Origin (0,0)	Bottom-left corner of substrate
Grid offset X	5.000 mm from left edge
Grid offset Y	5.000 mm from bottom edge
Cavity positions (X,Y) mm	(0,0), (5,0), (10,0), (15,0) / (0,5), (5,5), (10,5), (15,5) / (0,10), (5,10), (10,10), (15,10) / (0,15), (5,15), (10,15), (15,15)

3.0 CAVITY ARCHETYPES

Parameter	TYPE S (△)	TYPE M (◼︎)	TYPE L (▲)
Symbol	△	◼︎	▲
Diameter	3.000 mm ±0.005 mm	2.000 mm ±0.005 mm	1.500 mm ±0.003 mm
Depth	5.000 mm ±0.010 mm	10.000 mm ±0.010 mm	20.000 mm ±0.010 mm
Aspect ratio	1.67:1	5:1	13.33:1
Volume	35.34 mm³	31.42 mm³	35.34 mm³
Time constant (τ)	0.50 s ±0.02 s	1.00 s ±0.03 s	2.30 s ±0.05 s
Wall angle	90° ±0.3°	90° ±0.3°	90° ±0.3°
Wall finish	<0.1 μm Ra	<0.1 μm Ra	<0.1 μm Ra
Bottom finish	<0.2 μm Ra	<0.2 μm Ra	<0.2 μm Ra
Corner radius	<50 μm	<50 μm	<50 μm

4.0 CAVITY ASSIGNMENTS, CORE FFT (SPATIAL FREQUENCY DECOMPOSITION)

X (mm)	Y (mm)	Type	τ (s)
0	15	L	2.30
5	15	M	1.00
10	15	S	0.50
15	15	S	0.50
0	10	M	1.00
5	10	L	2.30
10	10	M	1.00
15	10	S	0.50
0	5	S	0.50
5	5	M	1.00
10	5	L	2.30
15	5	M	1.00
0	0	S	0.50
5	0	S	0.50
10	0	M	1.00
15	0	L	2.30

Kernal type, Symmetric Hankel. Anti-diagonals constant. Sensitive to spatial frequencies. No directional preference..

5.0 CAVITY ASSIGNMENTS, CORE GX (X-AXIS GRADIENT)

X (mm)	Y (mm)	Type	τ (s)
0	15	S	0.50
5	15	S	0.50
10	15	M	1.00
15	15	L	2.30
0	10	S	0.50
5	10	S	0.50
10	10	M	1.00
15	10	L	2.30
0	5	S	0.50
5	5	M	1.00
10	5	L	2.30
15	5	L	2.30
0	0	S	0.50
5	0	M	1.00
10	0	L	2.30
15	0	L	2.30

Gradient principle, Left columns (X=0,5) fast S-dominant, Right columns (X=10,15) slow L-dominant. Center transition M. Left heating → early output peak. Right heating → late output peak. Skewness proportional to ∂T/∂x,

6.0 CAVITY ASSIGNMENTS, CORE GY (Y-AXIS GRADIENT)

X (mm)	Y (mm)	Type	τ (s)
0	15	L	2.30
5	15	L	2.30
10	15	M	1.00
15	15	S	0.50
0	10	L	2.30
5	10	L	2.30
10	10	M	1.00
15	10	S	0.50
0	5	L	2.30
5	5	M	1.00
10	5	S	0.50
15	5	S	0.50
0	0	M	1.00
5	0	M	1.00
10	0	S	0.50
15	0	S	0.50

Gradient principle, Top rows (Y=10,15) slow L-dominant. Bottom rows (Y=0,5) fast S-dominant. Middle transition M. Bottom heating → early output peak. Top heating → late output peak, Skewness proportional to ∂T/∂y..

7.0 UWA-1

Component	Specification
Base	Pharmaceutical-grade paraffin wax, Tm = 60.0°C ±0.1°C
Latent heat	185 J/g ±5 J/g
Dopant 1	Pristine MWCNTs, Ø10-30 nm, L:1-10 μm, unfunctionalized, >95% purity
Loading 1	2.00 wt% ±0.05 wt%
Dopant 2	n-Tetracontane (C₄₀H₈₂), >99% purity, Tm = 81.0°C ±0.5°C
Loading 2	0.50 wt% ±0.02 wt%
Thermal conductivity (solid)	0.45 W/m·K
Thermal conductivity (liquid)	0.38 W/m·K

7.1 Composite Preparation

Step	Action	Parameters
1	Melt paraffin	82°C ±2°C, argon atmosphere
2	Add MWCNTs	High-shear 10,000 RPM, 30 min, 80-85°C
3	Add tetracontane	5,000 RPM, 15 min, 80°C
4	Ultrasonic probe	20 kHz, 100 W, pulse 5s/2s, 60 min, 78-82°C
5	Degas	<1×10⁻² mbar, 80°C, 60 min, until bubble-free
6	Store	Sealed, argon-filled, 6-month shelf life

8.0 INFUSION PROTOCOL

Step	Action	Parameters
1	Clean substrates	IPA ultrasonic, 40°C, 15 min → DI water rinse → N₂ dry → vacuum oven 120°C, 2 hr
2	Preheat	Substrate to 75°C ±1°C on vacuum hotplate
3	Evacuate	<1×10⁻³ mbar, hold 2 hr at 75°C
4	Introduce UWA-1	Via heated manifold, 75°C, sufficient to cover all cavities + 2 mm
5	Backfill	Argon to 2.0 bar absolute
6	Pressure hold	30 min at 2.0 bar, 75°C
7	Directional solidification	Gradient 5°C/mm across substrate thickness. Cool 0.20°C/min ±0.02°C/min from 75°C to 25°C under 0.5 L/min argon flow
8	Inspect	X-ray micro-CT, voxel <5 μm. Zero voids >0.01 mm³ in any cavity. Reject and rework if voids detected.

9.0 THERMAL BUSES

Parameter	Specification
Material	CVD single-crystal diamond
Dimensions	25.00 mm × 25.00 mm × 0.100 mm ±0.005 mm
Thermal conductivity	>1800 W/m·K
Electrical resistivity	>10¹² Ω·cm
Surface finish	<1 nm Ra, both faces
Quantity per core	2 (top incident face, bottom observer face)

9.1 Bonding

Parameter	Specification
Adhesive	BNNT-filled epoxy, 5 wt% loading
Bond line thickness	<5 μm
Thermal resistance	<0.1°C/W
Cure	25°C, 24 hr, vacuum compression 0.5 MPa
Post-cure	60°C, 4 hr, no pressure

10.0 PYROELECTRIC OBSERVER

Parameter	Specification
Material	z-cut LiTaO₃, single crystal
Dimensions	25.00 mm × 25.00 mm × 0.100 mm ±0.005 mm
Pyroelectric coefficient	>2.0 × 10⁻⁴ C/m²·K
Relative permittivity	46 at 1 kHz
Surface finish	<1 nm Ra

10.1 Electrodes

Parameter	Specification
Adhesion layer	Cr, 5 nm ±1 nm
Conductor	Au, 100 nm ±10 nm
Bottom electrode	Full-area ground plane, Z- face
Top electrodes	16 individual, aligned to cavities
Electrode sizes	S: 3.2×3.2 mm, M: 2.2×2.2 mm, L: 1.7×1.7 mm
Alignment tolerance	±10 μm to cavity centerlines
Patterning	Photolithography, lift-off
Edge pads	16 signal + 2 ground, 0.5×0.5 mm, 0.8 mm pitch

10.2 Poling

Step	Parameters
Temperature	85°C ±1°C
Voltage	100 V DC (Z+ positive), field = 1 MV/m
Hold	30 min at 85°C
Cool	1°C/min to 25°C under field
Remove field	At 25°C
Verify	Pyroelectric coefficient >2.0 × 10⁻⁴ C/m²·K

11.0 ENCAPSULATION

Parameter	Specification
Lid	CVD diamond, 25×25×0.100 mm
Seal adhesive	BNNT-filled epoxy, <10 μm bond line
Internal atmosphere	argon, 6N purity, 1.10 bar absolute at 25°C
Getter	Barium flash, 5×5 mm, activated post-seal
Leak rate	<1×10⁻⁸ atm·cc/s helium

12.0 CALIBRATION/ TFP...

Step	Action	Parameters
1	Uniform step	25°C → 30°C in <0.1 s. Record 16 ch at 200 Hz for 10 s. Extract τ per cavity. Verify within ±15% of nominal. Extract sensitivity (mV/°C).
2	Gradient GX	Left 30°C / Right 25°C. Record skewness. Calibration point +0.33°C/mm. Reverse for −0.33°C/mm. Fit linear model: ∂T/∂x = a·skewness + b.
3	Gradient GY	Top 30°C / Bottom 25°C. Record skewness. Calibration point −0.33°C/mm. Reverse for +0.33°C/mm. Fit linear model: ∂T/∂y = a·skewness + b.
4	Frequency sweep FFT	0.1, 0.2, 0.5, 1.0, 2.0, 5.0 Hz sinusoidal modulation. Record 16×16 coupling matrix.

12.1 Personality Map

• Core serial number and calibration date
• τ per cavity (16 values)
• Sensitivity per cavity (16 values)
• GX skewness coefficients (a, b)
• GY skewness coefficients (a, b)
• FFT coupling matrix (16×16)
• Thermal offset calibration

13.0 SYSTEM INTEGRATION

Parameter	Specification
Configuration	3 cores (FFT, GX, GY) on common thermal stage
Stage	Copper, Peltier-controlled, 25.0°C ±0.01°C
Spacing	10 mm between cores
Optics (optional)	Ge lens, f/2, 50 mm FL, AR 8-14 μm, FOV 30°×30°
Flex circuit	Polyimide, 18 μm Cu traces, 20-pin ZIF, 100 mm length, shielded
Readout	16-ch charge amplifier, 0.1-100 Hz BW, 200 Hz sample rate, 16-bit ADC

13.1 Output Vector

Quantity	Source	Units
Spatial frequency spectrum	FFT Core (16 components)	Normalized amplitude
∂T/∂x	GX Core	°C/mm
∂T/∂y	GY Core	°C/mm
	∇T
θ (gradient direction)	atan2(GY,GX)	radians

I'm trying to learn more about DSP. So I got DAFX by Udo Zolzer. Damn this book is awful!

It doesn't explain anything, it just gives the barebones mathematical definition of something and that's it.

For instance, I am not that familiar with Discrete fourier transform. All this book did was say that DFT can break down a signal into multiple frequencies and plot them on a spectrum. And it is O(N^2). And FFT is O(n log n). And give the equation itself.

THATS IT! It didn't bother to tell me what the transform was actually doing under the hood.

It was only after asking AI, it told me that the function compares the original signal to individual sine waves and examines how closely they match. That's how it gives you a idea of how much of each frequency is present.

Isn't this what a textbook is supposed to explain? This textbook (and almost all textbooks in this field) behave like pissed off high school students who are giving a presentation and barely giving a half assed attempt to ensure they don't flunk.

Hi guys, right now i'm about to finish my 2nd year in ECE at a college in Asia. I have been really enjoying the maths and courses like Digital Comm, Signals classes and a Network class. In my free time i also have been learning some hardware skills like using STM32 or FPGA though i do not enjoy as much as doing simulations.

I'm planning on taking a Master in another country and do some research in the field of Wireless communication. So, I have some questions.

1. Is the physical layer development research still relevant? Should I look out more for opportunity in layer 2 or 3 or higher? I have the impression that the physical layer research has been really repetitive and no new innovations.

2. What are some career options or jobs titles that I should look for? For example like embedded, DSP, network protocol engineer,...etc.

3. Is this field good to migrate to another country and what are some promising research in this area? (like maybe ISAC, QKD,...etc)

Thanks a lot for reading!

https://github.com/DrSDR/Radar-I-Q-Data/tree/main

please solve

English isn’t the first language for neither me, nor my teacher, and for some reason he thinks I’m good at this subject, so at this point it would be odd to ask. So just for clarity: “specs” are “specifications”? As in, type of filter, length, order, efficiency, maximum ripple, frequency ranges and such?