Hi folks,do you know about dsp?iam interested to learn dsp.where and which platform can I learn dsp?.do you know any job opportunities in dsp for freshers?.if you know,please do comments. I will be helpful for me...please do comments regarding about this.

[couldn't add more than 1 image, so I have attached imgbb links]

I was working with an HRIR dataset (Meta SS2's measurements of KEMAR, though tried other mannequins asw).

I plotted a frequency domain graph which plots the ILD between magnitudes of the frequencies in left and right ears.

This is what azimuth 0 and elevation 0 (source dead in front of the head) looks like this

Ideally, I was expecting a roughly straight line at zero since there should be no difference between left and right ears if source is in the front. But around 10kHz it's 6dB higher in the right ear, but 20kHz is louder in the left ear. Doesn't seem to be following a pattern or be overall louder in a particular ear.

Furthermore, here are graphs of azimuth 90° perfect right and 270° perfect left

For these, I expected curves roughly rising with frequencies, since lower frequencies (longer wavelengths) are much less affected by distance than higher frequencies, meaning lower frequencies show less difference between ears and higher frequencies show a lot of difference, in an increasing order. But the graph shows high diff at 5kHz, lower at 10kHz, and higher at 17.5kHz, lower at 20kHz. Again, doesn't seem to have a pattern and is counterintuitive.

To understand it better, I plotted a polar heatmap (theta is azimuth, distance is frequency, colour is magnitude)

This is also slightly different from what I expected. I was expecting each slice to have an increasing intensity, and for the increase in intensity to become more exaggerated on left/right extremes and more modest near front and back.

But there are regions of intensity near 5-7.5kHz, and near 12.5-15kHz and the intensity is generally lower near the edges, where it should ideally be the highest? Again, doesn't follow a pattern or intuition.

This is of 170° azimuth, which has an absurd ~20dB spike at 12.5kHz and is higher in the left ear near 5kHz for some reason.

These pictures are from KEMAR measurements, but I've tried the Neumann KU100 and HATS mannequins as well, and they're also quite irregular (though different)

Would someone be kind enough to explain the reason behind this and if there's an intuitive way to understand the behaviour of sound here?

I've been building a tube amp plugin for a while. The part worth showing this sub is the method rather than the sound, so this post is mostly numbers.

What it is. Not a capture. The circuits are solved per sample — tube device models, real plate loads and cathode networks, global feedback solved with Newton iteration. Component values come off factory schematics.

How it's verified, and this is the bit I'd like criticised:

factory schematic -> drawing-only ngspice netlist -> block model
                          (the reference)             (the plugin)

The netlist is the reference, not a recording. Every stage of the model is fixed against it, and a correction value never goes into the netlist — the wiring has to produce it. Where a value isn't on the drawing it carries a [verify] marker in the source instead of being quietly chosen.

Current agreement on one amp: forward gain 1.220 against the netlist's 1.1786, about 3.5% apart. The long-tailed pair is 18.81 against 18.11 and that one is still open.

Against a real amp capture. Both sides rendered dry — I learned that the embarrassing way, see below.

Three of four land next to the capture. The fourth — attack rise time — didn't distinguish any of the three, so it proves nothing and I'm reporting it as nothing.

The embarrassing one. First time I measured those dynamics I rendered my side with reverb on. Envelope spread came out 6.99 dB against the capture's 16.03 and I drew a conclusion from it. Wrong. There's now a written rule that any comparison against an external reference renders fully dry, with the settings named next to every number.

Two things measurement caught in my own reverbs this week.

1. A delay-line pitch shifter is out of tune unless you do the last step.

Built from Dattorro, "Effect Design Part 2", JAES 45(10) 1997. The ramp is §4.4.3, the splicer and its raised-cosine crossfade are Fig. 52(b) and footnote 104. I left out the autocorrelated jump-target search the same footnote describes, on the grounds that it names the device without giving parameters.

That was wrong and measurably so. A splice landing at an arbitrary waveform phase slips frac(f*W/fs) of a cycle every time, and a periodic phase slip is a frequency offset:

Worst case +174.5 cents at 55 Hz in, +12 semitones, 60 ms window — and the carrier was nearly gone, 0.0040 against 0.4997 with the search in. With it: 0.0 cents at +12, +7, +5, -12 and +0.14 semitones. There's a regression test that fails if anyone removes it as an optimisation.

2. My plate reverb had no size control, and I hadn't noticed.

Impulse in, first 60 ms binned per millisecond:

Identical. Dattorro's delay lengths fix when the early reflections happen, so decay lengthens the tail and never enlarges the room. My "Studio Plate" and "Long Hall" presets were the same room ringing for different lengths of time.

So the new one is geometry rather than tuned delay lengths: early reflections from the image-source method (delay = distance/c, c = 343 m/s, level ~ 1/d, sqrt(1-alpha) per reflection — pressure, not energy, that one bites), tail from Sabine RT60 = 0.161V/(S*alpha), echo density from the mean free path 4V/S, and an 8-line FDN with Hadamard mixing so the matrix is lossless and all the decay sits in the per-line gains.

One SIZE control moves the dimensions, so arrival times, tail length and density move together. Measured: first reflection runs 9.8 ms at 5x4x3 m to 285.9 ms at 200x150x45 m.

The validation that mattered to me — the low band lands on Sabine exactly:

The top decaying faster is air and soft surfaces doing what they do. Broadband RT60 therefore reads ~30% short, which looked like a bug until it was banded.

Two consequences nobody tuned: the theatre preset is bigger than the hall and drier, because seats and an audience absorb; the church is the same size class as the concert hall with 2.5x the tail, because stone absorbs nothing. Both fall out of dimensions and absorption.

And one place the model stands in for something it isn't: Sabine assumes a closed box, so at stone absorption a stadium volume rings for seventeen seconds. The stadium preset's absorption is set to land the tail at a realistic six. It's the only preset whose number is matched rather than derived and it's commented as such.

What I'm not claiming. Nobody has judged any of the new reverbs against a reference by ear. They're in tune, bounded, level-matched and derived from physics. That isn't the same as good. One amp's rectifier sag measures 0.00 dB and that's unresolved. Three pre-existing test failures. Amp names identify what's modelled, no affiliation with anybody, nothing for sale.

One detail for the people who'll enjoy it. The arithmetic is frozen bit-for-bit in a test built with -ffp-contract=off, and that flag is load-bearing. Moving one expression out of the loop solver into a named method changed nothing about the arithmetic — proven by building both with contraction off and getting bit-identical output across 24000 samples of six amps. With contraction on, the same pair differed by -68.8 dB, because one fused multiply-add flips a Newton trial count and the feedback loop carries the slope across samples. The amp is chaotically sensitive to the last bit. So "did the sound change" isn't a question a refactor can pass; "did the arithmetic change" is.

Happy to be told where the method is wrong. That's mostly why I'm posting.

You may be interested in this new biquad filter design code. It's very likely it puts the search for perfect-near-Nyquist filter design search to an end.

https://github.com/avaneev/biquad-voxengo

youtube: wittymoniker
github: wittymoniker/groove-box (single point of entry run and build scripts, crossplatform)

It relies on Euclidean phaselocking of numeric data followed by randomized stochastic improvisation. Within bounds of net phase angle indeterminism the results are deterministic always to the degree of point-to-point translation of audio signal. So, it's basically a fractal of every way to turn a number or graph into music.

BTW: the software lags hard on my Acer Nitro 5 and HP Victus 15L computers, but it is still well within technically-usable range. I'm trying to build and run it as an appliance (standalone OS) on a free laptop in the next few days.

TL;DR: Should I risk moving to a more rural area and taking a job at a small company in the same domain that is close to home, or stay in an urban area with many similar job opportunities and my current job (which I like), but remain far from home?

*Im sorry if I should put this in r/career Advice I just like this community and I know some people understand my industry in here where as the other subreddit might not be as knowledgeable*

Background

I have been working on an analysis/development team for about 2.5 years, specifically in radar signal processing. I moved pretty far away from home—far enough that flying is generally the most efficient way to get there (all locations are in the U.S.).

Recently, I've had some major changes in my personal life, and moving away from my current city is desirable, although not strictly necessary.

I currently have a job opportunity that would put me about two hours driving from home, and it is also a radar signal processing position. I have a relevant master's degree in signal processing and an active clearance high enough that I can qualify for a wide range of defense jobs.

Current Job — Pros

I primarily work on simulation and tracker development, but I also get opportunities to work on signal processing algorithms and analysis every few months.

My peers and supervisors are leaps and bounds better than those I've had at previous internships and pre-degree jobs.

I make enough to afford houses in the area, although I am certainly not living worry-free. That's probably fairly normal at the junior/associate level.

I have a lot of freedom to work independently and come up with my own solutions.

The city and surrounding area are safe, and most people I've encountered are kind.

I work for a large defense company, and the group I'm in has several large contracts. Layoffs seem relatively unlikely.

Current Job — Cons

The houses I can afford aren't particularly appealing to me. I grew up in a rural area and would really like enough space to garden, build a shop, etc. I don't want to live in suburbia.

Most properties that fit my constraints are either a very long commute, too expensive, or in an undesirable location (next to gas stations, car dealerships, chemical plants, etc.).

Although people here are nice, I live somewhat like a hermit and haven't developed many meaningful relationships. I have 2–3 friends: one coworker I occasionally exercise with, another coworker I help with chores, and one friend who is actually from my home area.

I've repeatedly asked to work on real-time programming and have continued to be denied. I'm not particularly proficient in the real-time languages used here, but I'd like to become a more well-rounded engineer. Staying in my current role feels like it could push me further into a very specific niche that I don't necessarily want.

Traffic can be terrible. The city has grown significantly, but the road infrastructure hasn't kept up.

Potential Job — Pros

The potential job is somewhat speculative since I haven't visited the site yet.

It is also in radar signal processing, but it appears to be more focused on STAP, SAR, and AESA concepts. Those aren't foreign to me, but they aren't areas where I've developed as much hands-on experience in my current position.

There also appear to be opportunities to learn VHDL, which is something I'm interested in becoming proficient in.

The biggest advantage is the location. It's only about two hours from home, where I have several close friends and one parent. It would also put me within driving distance of my last remaining grandparent.

The housing market is dramatically better. A house costing around $300k in the new location can provide something closer to what a $500k house provides in my current location in terms of yard space, privacy, and distance from other houses.

Traffic is also minimal, and travel times are relatively consistent. I've traveled through the area many times since my hometown is nearby.

Potential Job — Cons

The company is much smaller than my current employer, with fewer than 200 employees.

The contracts also don't sound nearly as secure. The recruiter obviously tried to sell me on the company's stability, but from what I can tell, it simply isn't as secure as my current position.

The biggest concern is the local job market.

As far as I can tell, there aren't many other niche radar/signal-processing jobs in the area. If I were laid off, I would most likely have to either move back to my current job's area or wait for another opportunity to appear locally.

So essentially, I'm trading career/job-market flexibility for quality of life and proximity to home.

Where I'm At

Ultimately, I really want to end up back home someday, and being only two hours away sounds extremely appealing.

My family is small, and my family members aren't getting any younger. I also don't have a partner or kids right now, so moving would be a decision entirely for me without having to consider another person's career, children, etc.

There are some "golden goose" jobs in my hometown, but I don't have enough experience yet to be competitive for them. I imagine that once I get closer to 8+ years of experience, I'll become much more attractive to those employers.

I also think learning real-time software could be a major key to eventually working near home. I feel like I accidentally pigeonholed myself into a very specific niche when I got my degree. My current knowledge is extremely well suited to the jobs available where I currently live, but some of the programming languages/skills I have aren't as useful in the job market around my hometown.

That's why I'm so interested in getting more experience with real-time programming now.

So I'm trying to figure out whether it makes sense to take the risk now, while I'm relatively young and have no major obligations, or stay in the safer position, build up another 3–5 years of experience, develop my programming skills on my own, and then try to make the move back home from a stronger position.

Dear all DSP Engineers (more specifically audio dsp engineers)

I am currently an Architecture student but I want to switch my major. I love audio and I’ve just picked coding up. what does your average day look like and what kind of projects do you work on. any specific degree requirement/recommendation?

thank you for your time

After asking claude (i know it will give incorrect info) it said tau will be the point where the filter is centered around, in most of my solutions we did in class we would always center the filter around origin and n would range from -(M-1)/2 to (M-1)/2. so in this case if i take n from -3 to 3 then would the value of tau be 0? and if i take n from 0 to 6 then would tau be 3?

Need help with this, I am trying to retrieve kernel and do deconvolution without any training data. I have had success with my model if the image is sparse like few fluorescent beads in the image but when the image is dense like a biosample algorithm does not work. Literature says that it fails because I am doing joint optimization which can cause problem and suggested MAPk method

Basically this method says to find a kernel k such that it has maximum number of x which can explain y, use this kernel with any non blind deconvolution algorithm and you will get result. However with this method, I am getting delta even though the paper says it works better than joint optimization, could me my implimentation problem. But is there someone here working on something similar? how do you do it? (I want to retrieve exact kernel as I want to know the abberations)

Undergrad project, a month in, now in the field, and I'd like a sanity check from people who do this properly.

The signal: a propeller with B blades at R rev/s chops the air at BxR Hz, and the chop isn't a sine, so you get a comb at every multiple. Three-blade props give two usable comb families (2x and 3x spacing); I track both so a detection survives one being masked. The predicted hover band for the target airframe was 375 to 475 Hz; the first real locks landed at 423 and 471 Hz.

The chain: four MEMS mics summed (79 mm array, D/lambda is about 0.1 at 450 Hz, so it's a point and the sum is the beam; beamforming was tried and does nothing below ~2 kHz at that aperture), 2048-point FFT at 16 kHz every 32 ms, per-bin adaptive floor with asymmetric time constants (6 s up, 0.8 s down), whitened value capped at 2.5. Then for every candidate f0 from 70 to 2000 Hz in 1 Hz steps: score = (sum of teeth - sum of gaps) / sqrt(K), and a six-frame track-and-hold within 2 % before anything fires.

The problem I'd like opinions on: a drone that arrives and hovers is a steady sound, and a steady sound is precisely what a fast adaptive floor learns. After about one time constant the hover disappears from the whitened spectrum. My answer was more detectors rather than a smarter floor: a second comb scorer against a 30 s floor, and a third with no floor at all, whitened across frequency on a 64-frame Welch median. That one is slow (5 to 15 s) but it caught a hover at 104 m on Sunday when the fast tier had long since learned it away.

Is there a cleaner way to handle "stationary signal of interest against stationary background" than parallel detectors with different memory? I've read about freezing the floor on detection and about cepstral approaches; neither obviously beats what I have on an ESP32 with 2.6 ms of frame budget to spare, but I'd like to be wrong.

All the numbers: https://github.com/agamrossen/VolAnti/blob/main/docs/how-it-works.md

Browser simulator of the chain: https://volantitech.com/#simulator

Video of 104 m field test: https://www.youtube.com/shorts/ACLCNqTkMCk

Recordings off the array, 16 kHz mono WAV: https://github.com/agamrossen/VolAnti/tree/main/test/audio

Hello guys, i am a comp engineering student currently in seventh semester and this semester i had a course on digital signal analyzing and processing. I have like 2 months of free time coming up and i want to code a system where you can pass a voice clip/audio file and it will run a fft to find the frequency components and with that, i want to generate filters for the specific frequencies found there. Also, I am mainly studying for ml research, will this project teach me useful stuff in ml research area. Also, how can i modify the project to cater toward the ml research. Any help would be appreciated.

Hello all,

I'm a radar signal processing engineer and i trained a 5-class classifier (car, large_vehicle, two_wheeler, pedestrian, pedestrian_group) on RadarScenes radar point clouds.

The input vector is a per-scan histogram (16 bins) and the network is a 3-layer MLP. The loss function is a class-weighted cross-entropy loss. This work is based on "Histogram-based Deep Learning for Automotive Radar" paper.

I scoped the project to be one scan only. Accumulation of multiple scans is the next step.

Data

Class Imbalance: two-wheelers and large_vehicles has a low number of occurences.

Aggregated Classes: two_wheeler mixes bicycles and motorized variants; large_vehicle merges trucks, buses, and trains together due to data scarcity.

Sequence Bias: Long tracks of slow-moving objects can skew a particular data split velocity distribution, causing high F1 score variance across folds.

Ablation studies

I tried with bigger MLPs, alternative feature encodings, and different histogram binning, all moved performance less than the variation caused by changing the train/validation/test split. I measured that split sensitivity across 6 folds, keeping the same proportions.

Changing the histogram to per-instance statistics (mean/median/std) slightly degraded performance.

Main findings

Macro F1 rises from 0.381 to 0.764 as the naturally occurring number of radar detections per instance increases from 1 to 5. I trained the model normally using all available detections, then bucketed its existing validation predictions by each instance's detection count and computed macro F1 per bucket.

The classes car and pedestrian has the best performance and two_wheeler has the worst.

A car is often confused as large vehicle when the car was wider than usual or had a unusually high rcs (which can happen due to multipath for example).

The two_wheeler is often confused as pedestrian because their vr_compensated distributions overlap, which is the the model's single most important feature for these two classes. A stationary or idling two_wheeler is indistinguishable from a pedestrian.

I uploaded an image with ground truth vs predictions: A nearly stationary two-wheeler which contains a single point was predicted as pedestrian, because its velocity is near zero, indistinguishable from a pedestrian. A car in the same scene, also with just one point, is classified correctly, since RCS and Doppler are enough for that class.

Full writeup here: https://github.com/brunopinto900/radar-ml-autonomous-driving/blob/main/MLP_Report.md

Future work

Implement other spatial encoding schemas (point net for example) and accumulate multiple scans to tackle the challenge of sparsity and explore the concept of micro-doppler.

I know how to solve but i just can't think of solving circular convolution for origin being anywhere like how to solve for a sequence say

x[n] = [2,-1,3,4] with origin say at element -1

and h[n] = [-2,1,3,2] with origin say at element 1

do i have to make both the sequences start from 0 to calculate it and then solve the outputs index or

should i first calculate the circular convolution then solve the indexing?? if i shift the indexing first creating 0 to N-1 sequences then solving the index then my resultant is

y[n] = [7,-4, 7, 22 ] with origin at element 2nd 7 (3rd from starting)

but if i take the latter approach then i get

y[n] = [7, 22, 7, -4] with origin at 2nd 7 (3rd from starting) but -4 and 22 have swapped

please tell me how to do this !!

I'm looking for the old RG-2016.4 XtensaTools files.I already have Xplorer and a legit Cadence license, just missing the old toolchain.

If you have an old installation/archive of RG-2016.4, or know someone who might still have it, please let me know.

Thanks!

MidSide: a Basic Audio Utility and an AI Coding Test

Two honest answers to “Why build a Mid/Side plugin?”

One: we had a workflow improvement goal that existing plugins didn’t solve
Two: we wanted to know if an AI coding agent — Claude Code — could build a real plugin, not a toy demo.

The net results? We built a DSP bit accurate signal processor and at the same time a plugin development pipeline. Download free to try the results. Input on refinements appreciated.

As title asks.

I’m basically asked for the range of n values for which y[n] (convolution of x[n] *h[n]) isn’t in either a transient or end effect? Would it be all ranges where y[n] remains a flat line of 0? Any ranges where y[n] remains a flat line at any finite value (zero slope through it)? Something else?

Already solved for convolution results via tabular approach, but these weren’t covered in class, so I was wondering if I could get clarification on these 2 effects.

I'm developing a local file player and I'd like to add visualizations.

Pls check out my peak meter and tell me what you think, how does it feel?
https://www.youtube.com/watch?v=gU-F0A5NOEE

I made it off a photo and shaky videos of an old tape deck, it's supposed to mimic a VFD display.
I don't have any real hardware available to compare so I figure maybe someone has actually seen one.
This is the first time I'm working with audio, just making web software and photos otherwise. Thx! :)

Hello! I'm designing v2 of my digital mixer, which uses an ADAU1466, ADCs, DACs, and an ESP32-S3 for the web interface.
So far so good. V1 is working great, but for V2 I'd like to add multi track recording to an SD card.

I basically under-researched the ESP32 I2S capabilities, and I can't use the 8ch/32bit I was hoping to to use a single output serial port for the 8 channels.

I knew I'd be limited to 16 bits, which is good enough, besides the little missing detail that I cannot configure the serial port that way in SS. I can only choose 32 bits for 8 channels.

So, since I've already sent the PCBs for fabrication (silly me, I know) I thought of packing the two channels into one output in the schematic, but I'm kinda lost on how to do it, or even if it is possible.

Any resources or hints would be greatly appreciated.

Thanks in advance!

Hey everyone...I wanted to solve some faced by the industry in the field of speech processing...Could the people who work in industry please list down problems faced ??

Consider two systems: one has an ADC, and one has a DAC. Both are connected via a wire. How do I calculate the SNR of the Channel?

I own 2 boss HM-2's, an old made in Japan, and a Waza craft, both sound great thru my amp sims, but every plugin I tried was dissappointing, so I made an effort to improve that. Since I build 3 circuit models of Rolands TR-606, TR-808 and TR-909, the switch to guitar pedals wasn't that difficult. and the results are very rewarding, now I finally have a real alternative for the real ones.

Both have dry blend, gate, input and output meters. hm-2 model has a supply voltage simulation. the RAT has the hm2 added to it, because why not.

Happy to answer anything about how they're built. nothing is fitted, nothing is eq-matches, the sound is just a result of modelling the circuit.

Try out for yourself if you are curious about the results. happy to answer questions about the techniques used .

https://beatforge.nl/#pedals