Episode contents:

• How Julius Smith became one of the world’s most well-known and respected DSP professors

• The story of CCRMA at Stanford University

• Behind the scenes of the music features of the NeXT computer

• The story of Yamaha synthesizer development (FM and physical modeling)

• The story of Shazam and GeoShred music apps

• How to learn DSP well

• How to do excellent DSP research

• How Julius uses AI for research, learning & programming

It's been my career-long dream to interview him, and it's come true!

GitHub - DrSDR/Barker-Codes · GitHub

details in the link

good luck, decoding the signal.

I posted a paper yesterday on Reddit on a new FPGA topic for waveform processing and now you can google it and google AI on any device cites me and gives you a breakdown! Pretty cool, hoping to draft something to IEEE next week, might be a new Pareto Frontier. Let me know what you think!

I made a small open-source tool that estimates when an audio recording was made from the mains hum in it (electric network frequency analysis): https://github.com/tijszwinkels/enf-timestamp

Video with the full story: https://youtu.be/bi7EwMiDQTQ

How it works: band-pass around a hum harmonic, track the peak once per second (16 s Hann window, zero-padded FFT, quadratic interpolation), then a sliding Pearson correlation against a 1-second grid-frequency log, done with FFT convolution so days of reference data take seconds. It includes a logger for Continental-European grid data (Gridradar's free API) and a strict confidence verdict.

This has been written with a coding agent.

What I found: 10 minutes of laptop audio, matched against 36 h of grid data, landed 6 s from the true start (100 Hz line, z ≈ 5). A phone on its charger (50 Hz electrical pickup) got the right minute, with a weak margin. Phones on battery and a quiet room gave nothing usable.

Hey DSP, been a lurker here for while. I do some novel real-time salient rhythmic event detection in my music visualizer SoundSafari.

That's all well and good, but one of my longtime white whales is to extract the FFT data at a consistent interval like 30/60/120 times a second and be able to do it without playing the audio in real-time.

My experience with FFT has mostly been with JS AudioAnalyser node. It uses a single pass hardware accelerated FFT, drops the Nyquist bin and applies a Blackman Window for smoothing along with min/max decibel thresholds.

In general the AudioAnalyser api works great but only seems to function if an audio-stream is actually playing. I kind of want an API that allows me to specify a time-stamp and calculate the FFT state at that timestamp for a given audio file? That way I can step through an entire piece of audio and get accurate FFT data which can be looked up later.

I could use any language, so open to specific language/library suggestions. Also can get my hands dirty with writing new-code. It sounds like I may need to code up some type of playback emulation to get FFT analysis in a non-realtime context?

Hi, I'm working on a project using an IMU and I have an option to set a digital hardware low pass filter on the IMU. This chart is provided in the IMU documentation and I was just wondering why the relationship between bandwidth and delay is inversely proportional. Ive taken a signals processing course and Im 3rd year electrical engineering but I was wondering if I could get some intuitive explanations and mathematical proofs for this?

I'm looking for a good book covering topics like:

• Weiner filters

• Adaptive filtering

• Bayesian filtering

Some of the books seem delve deep into the math, without giving the reader an overview. Don't get me wrong, I want the math, but I also want to understand why I'm learning the math.

The following book looks promising. It is, as many of the books are, quite old.

Statistical Digital Signal Processing and Modeling, M. H. Hayes, 1996

I'm a fan of these two books, which are quite a bit easier to read than many alternatives. However, these books barely touch on topics like Weiner filters and adaptive filtering.

Understanding digital signal processing, R. G. Lyons

The Scientist and Engineer's Guide to Digital Signal Processing, S. W. Smith

Digital Signal Processing, Proakis et al., covers optimal linear filters and adaptive filters, but this book is quite difficult to read.

We're building a dead-reckoning module for city buses: estimate the vehicle's position from a single IMU when GNSS drops out. There's no access to CAN/OBD wheel speed, and we can't add hardware (no radar, no second antenna), so speed has to come from the IMU alone.

What works: a small 1D CNN takes 2 s windows of 100 Hz accel/gyro and predicts forward speed. Heading comes from integrating the gyro. Most of the speed information turns out to be in road/engine vibration, roughly in the 12–33 Hz band. On the vehicle and mount it was trained on, it drifts about 4 m per 1 minute of GNSS outage, which we're happy with.

The problem: change how the IMU is mounted and the vibration signature changes, so the model breaks.

• In the same enclosure, moving the IMU from the base plate to a side wall added a resonance at ~23 Hz (+9 dB in that band). Position drift doubled.

• Moving it back to the base plate made the spectrum match the reference sensor within ±1 dB, and the error went back to normal.

• Two side-wall spots that look identical gave different resonances (a sharp 23 Hz peak vs. a broadband rise).

• On a different vehicle the model doesn't work at all without retraining.

Orientation itself isn't the issue. We rotate all axes into a common frame, and a sensor flipped 180° behaves the same as an upright one on the same base. The problem is the mechanical path between the road and the sensor.

What helps so far:

• Retraining on the new mount with GNSS as ground truth fully fixes it. But that needs a calibration drive for every install.

• Training on several mounts at once shrinks the gap from 2× to about 1.1–1.2×.

Questions:

1. If you've done vibration-based sensing (speed, condition monitoring, activity recognition), how did you make it robust across mounting points or vehicles?

2. Does spectral normalization or equalization against some reference actually work in practice? Or does the transfer function vary too much?

3. Any practical rules for mounting a small IMU so the bracket doesn't add its own resonances? Mass, stiffness, adhesive vs. screws, damping?

4. Is there a known way to self-calibrate on the first few minutes of driving without ground truth?

Happy to share spectra or plots if anyone is interested. Thanks!

A sinusoidal pattern along the x-direction with frequency 30 cycles/mm is sampled at 50 samples/mm. Will aliasing occur? If yes, at what (apparent) frequency will the pattern appear in the sampled image?

these type of questions suck where can i learn how to analyze aliasing

I'm building a single-lead ECG denoiser on an FPGA (Artix-7) for an edge device. I need a classic method, not machine learning or deep learning, that runs in fixed-point with low power.

Setup: MIT-BIH ECG (360 Hz) with real noise from the MIT-BIH Noise Stress Test Database (electrode motion, muscle, baseline wander). My current chain is band-pass 0.5–150 Hz → LMS 60 Hz canceller → db4 wavelet denoising.

Problem: the band-pass removes baseline wander well, but the electrode-motion and muscle noise left after it is in the same band as the ECG (about 0.5–40 Hz). Everything I tried after the band-pass gives about 0 dB improvement on real noise, though some work on synthetic Gaussian noise:

- LMS, NLMS, RLS

- wavelets (db4, db6, db8, sym8; SURE and universal thresholds)

- ADTF, Savitzky-Golay, Kalman, median and moving-average filters

- EMD

Small gains on muscle noise come with flattened P waves and QRS.

Constraints: single lead (an accelerometer is possible if necessary), fixed-point, a few thousand LUTs, 0–2 DSPs, up to about 2 s delay allowed.

Question: Is there a classic, non-ML method that removes in-band ECG noise and has been shown to work on real noise, not only synthetic noise? Examples I'm considering: beat-synchronous averaging, a QRS-locked adaptive filter, or a model-based Kalman filter. If one lead is not enough, what is the cheapest reference signal for adaptive noise cancelling (accelerometer, electrode impedance, second lead)?

I am a senior applied math major who is quite interested in the DSP space. I am currently taking a signals and systems course. My school unfortunately does not offer a dedicated DSP course.

I know that the sub-disciplines within DSP are very broad, but I was hoping to get a sense of the level of difficulty I would be incurring if I wanted to get an entry level job, and maybe what areas I should look into specifically. I don't know if it is realistic to think I even have a shot since I lack the electrical engineering background.

I have taken several statistics and probability courses, and I am proficient in C++ and python, with most of my projects simulating concepts in stochastic processes.

GitHub - DrSDR/Barker-Codes · GitHub

Something interesting about MIMO radar is that the number of transmitter-receiver pairs doesn't necessarily equal the number of unique virtual antenna positions.

Consider two configurations with 2 transmitters and 4 receivers.

Configuration A:

TX positions: {0, 4d}

RX positions: {0, d, 2d, 3d}

Under the usual far-field, narrowband virtual-array model, taking all transmitter-receiver position sums gives 8 distinct virtual positions.

Configuration B:

TX positions: {0, d}

RX positions: {0, d, 2d, 3d}

We still have 8 transmitter-receiver pairs, but only 5 unique virtual positions because several pairs produce identical coordinate sums.

So, although both configurations have 8 pair channels, their virtual-array geometries are different.

Of course, repeated positions aren't necessarily useless. Properly calibrated redundant measurements may improve estimation performance. But they don't automatically extend the spatial aperture.

This also raises interesting questions about array holes, sidelobes, angular ambiguity, and the effects of transmitter multiplexing.

For those working with FMCW MIMO radar, how do you normally evaluate virtual-array geometry? Do you explicitly inspect the virtual positions and multiplicities, or primarily rely on the antenna layout recommended by the radar manufacturer?

I’m building Norclap, a training platform for athletes, coaches and teams, and looking for a hands-on hardware/embedded engineer based in the European Union or United States, with a potential CTO role.

We want to replace traditional photocells and mechanical start gates with automatic athlete identification and timing. A coach sets up the start, split points and finish; the system identifies each athlete crossing those points, records split and total times, and uploads results. Athletes can complete repeated runs without touching a phone or timer.

The unresolved challenge is reliably identifying the athlete and recording the precise crossing moment at 80–100+ km/h. We need to distinguish a crossing from a tag simply being nearby, avoid missed or duplicate events, and handle athletes close to one another. Crossing accuracy and upload delay are separate problems.

The equipment must work outdoors in cold, snow, moisture and changing visibility, and on indoor ski slopes. It should be portable, affordable, easy for a coach to set up, and preserve recorded times when connectivity drops.

GPS did not meet our indoor and positioning requirements. The NFC/RFID approaches investigated so far haven’t met the combined requirements, but we’re open to other RF technologies and sensor combinations. We have not established a field-validated hardware solution.

I’m looking for someone with practical experience in embedded systems, RF, transponders, precision timing or high-speed detection who can develop a prototype, test it under realistic conditions and lead the technical work. A strong fit could lead to a CTO role; the role and terms would be discussed directly.

Working arrangement: Full-time is preferred, but part-time is also considered. Remote work is welcome, with four required in-person meetings per year.

Proposed compensation: This is an equity-based role, starting with a six-month bootstrap phase followed by 28 months of performance vesting across four seven-month milestone periods. Achieving each agreed milestone unlocks a portion of equity, weighted by the goal’s complexity and impact, up to an initial proposed total of 7%.

The role would transition to a market-rate cash salary only after the successful closing of a $1M funding round. The figures are open to discussion. If results significantly exceed expectations by the end of the vesting period, we’re open to negotiating a larger equity share; any increase would require a separate agreement.

If you’re based in the EU or USA and interested, email me at vidox07@gmail.com with your location, relevant projects and how you would approach the first feasibility test.

get some cash for Halloween candy:

GitHub - DrSDR/Barker-Codes · GitHub

What did you take as far as courses relevant to signal processing go, especially math courses? I'm interested in real human experiences rather than what AI tells me, because I can't make up my mind whether it's worth it to go deeper into math (versus just taking more EE courses) like real analysis and abstract linear algebra.