r/DSP

I tried to create sine waves using DDS and 8 bit R-2R DAC. The output waveform is like this instead of a sine wave is there anything I'm missing here?

This concept album was an opportunity to use almost at random 10 chosen soft synths in different combinations, no less than 2 at once, no more than 5.

"Musicalions" is another term for the unknown entities at work when in specific conditions, the power of 2 or more synthesizers are harnessed together.

Their presence is usually detected by sudden, rapid burts or pauses, sheer intensity, unusual beauty and experimental musical arrangments.

This late night musical "channelinning" procured the raw material for the album. Each track was performed, edited and produced by myself in FL Studio 20.

The soft synths used were:

Sugar Bytes Aparillo

Sugar Bytes Unique

Roland XV-5080

Roland Pro Mars

Roland System 100

Spectrasonics Omnisphere2

Spectrasonics Trilian

Traktion F'EM

Image Line Harmor

most tracks were tweaked extensively on the go during recordings to keep the sound unpredictable.

A big thumbs up to the developpers of these incredible instruments!!!

I hope this is but a start in the production of such tools, without you, there would be no "Musicalions" tonight:)

https://youtu.be/cakuN2yPWFg?feature=shared

I've noticed a strange audio artifact in Roblox, especially in older games/weapons that rapidly retrigger short sound effects.

At lower rates, I hear the individual sound events normally. But once the sound is retriggered rapidly enough, a separate continuous tonal/harmonic sound appears underneath the SFX. It sounds roughly F#, and it behaves almost like a background hum or ringing generated by the audio system.

The strange part is that it doesn't seem to behave like ordinary repetition pitch. Increasing the firing/retriggering rate further doesn't simply make the tone continue rising in pitch. After crossing a certain density, the underlying tone remains approximately stationary while the individual sounds continue being retriggered.

I've reproduced it with very different Roblox sounds: rapid gun SFX (including a modded gun using different samples) and an unrelated rapidly repeated punch/impact SFX. The same kind of underlying tone is audible in both.

48,000 / 1024 = 46.875 Hz, but I don't know whether that's actually related to Roblox's audio processing or is just a property/artifact of my recording chain.

Does anyone familiar with Roblox's audio engine know what could cause this? Is there some voice scheduling, mixing, block processing, quantization, clipping/limiting, or other DSP behavior that becomes audible when Sounds are retriggered extremely rapidly?

Hello all,

I built a radar object classifier on RadarScenes, extending a prior single-scan classifier to accumulate observations over a tracked object's history instead of classifying each scan in isolation.

A single RadarScenes object instance contains only about 2.9 radar points on average, very sparse. A single scan also can't capture temporal characteristics: RCS and micro-Doppler both vary continuously as an object moves. Pedestrians produce characteristic micro-Doppler from limb motion; different object classes show different RCS fluctuation patterns as aspect angle and scattering geometry change scan to scan. Accumulating observations gives both higher point density and provides temporal dynamics.

Multi-scan baseline

DeepReflecs encoder (Ulrich, Glaser & Timm, RadarConf 2021), PointNet style, per point shared weights, on single scans across car, large_vehicle, two_wheeler, pedestrian, pedestrian_group: 0.7370 macro F1.

Using RadarScenes' persistent `track_id`, I build a causal, N=20, per track sliding-window buffer:

- x_seq/y_seq: Global, odometry-corrected coordinates recentered per scan on the object centroid. Unlike x_cc/y_cc (car-frame coordinates that accumulate over time to form a trajectory).

- Cross sensor buffer: whichever of the 4 sensors currently observe the track push to the same buffer.

- Stride 1, causal: every new scan updates the buffer and produces a prediction. No future context, real time streaming compatible.

- Each scan is encoded once by a frozen per scan encoder and cached

- Fusion concatenates the causal GRU's hidden state (order aware) with an order-invariant pooled embedding (all N scans' points as one set, no sequence structure) through a small trained mlp head.

Results

Model	Macro F1	Delta
Single scan	0.7370	(baseline)
20 scan point pooling	0.8613	+0.1243
Causal GRU	0.8895	+0.0282 over pooling
GRU + pooled embedding (fusion)	0.8897	+0.0002 over GRU, noise

Pooling alone, no sequence model, no notion of scan order at all, recovers +0.1243 macro F1. The GRU adds a real but much smaller +0.0282 on top. Fusion adds nothing measurable beyond the GRU.

Ablation

Llarger GRUs, a Transformer, a state space model, point level self attention, all trained on the exact same frozen per scan embeddings, land inside a 0.86 to 0.89 band, a 0.03 spread. End to end fine tuning of the frozen encoder makes things slightly worse (about -0.002 to -0.003), not better.

Conclusion

In this setup, the largest gain comes from giving the model more observations of the same tracked object: 20-scan point pooling improves macro F1 from 0.7370 to 0.8613 without using scan order at all.

Temporal modelling then provides a further, meaningful improvement. The causal GRU reaches 0.8895, adding +0.0282 over the pooled representation. So temporal ordering clearly contributes useful information; it just accounts for a smaller portion of the overall gain than observation accumulation.

With the per-scan encoder frozen, the different sequence architectures tested, suggests that the quality of the per-scan representation is the bottleneck than the particular mechanism used to aggregate the sequence.

Full report, every ablation, confusion matrix, coordinate frame reasoning: https://github.com/brunopinto900/radar-ml-autonomous-driving/blob/main/final_report.md

Thank you.