# hukenovs/dsp-theory

Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.

Repository: https://github.com/hukenovs/dsp-theory
Canonical: https://ross.abutalabs.com/products/dsp-theory
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Topics: digital-signal-processing, fast-fourier-transform, finite-impulse-response, fir, fft, convolution, python, numpy, scipy, numpy-tutorial, dsp, fpga, lessons, tutorial, lectures
Last push: 2026-04-21T18:12:35+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 35, longevity 100
- inputs: {"age_days": 2611, "days_push": 134, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1233, forks 204 (observed 2026-08-28T04:04:04.533577+00:00)

## What it is
A collection of Jupyter Notebook lectures on digital signal processing theory, covering signals, Z-transform, DFT/FFT, convolution, modulation, IIR/FIR/CIC filters, windowing, and resampling. Written in Python with numpy, scipy, and matplotlib, based on university lectures (in Russian).

## Use cases
- learn digital signal processing with python
- understand FFT and Fourier transforms
- study IIR and FIR filter design
- learn about convolution and correlation
- understand signal modulation AM FM chirp
- learn CIC filters and resampling
- dsp tutorial with jupyter notebooks

## When to choose
- you want a structured, notebook-based DSP course with runnable Python examples
- you need theory plus practical numpy/scipy implementations of filters and transforms
- you read Russian and want university-level DSP lectures

## When to avoid
- you need English-language material
- you want production DSP code or a library rather than educational notebooks
- you need FPGA/Verilog implementations despite the fpga topic tag

## Facets
- artifact type: learning-resource
- maturity: active
- function: audio-processing, data-visualization, developer-tools
- domain: tutorials, education
- platform: python, cross-platform
- tags: digital-signal-processing, jupyter-notebooks, dsp, fft, filters, signal-processing, lectures, russian-language, algorithms, audio

## Member repositories
- hukenovs/dsp-theory (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.533577+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:16.406244+00:00, confidence not recorded.
  - readme: https://github.com/hukenovs/dsp-theory (fetched 2026-08-28T04:04:04.533577+00:00, sha 5a4f82ae0059)
- Data as of 2026-08-30T08:39:29.467469+00:00.