# musikalkemist/AudioSignalProcessingForML

Code and slides of my YouTube series called "Audio Signal Proessing for Machine Learning"

Repository: https://github.com/musikalkemist/AudioSignalProcessingForML
Canonical: https://ross.abutalabs.com/products/audiosignalprocessingforml
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-07-19T15:40:39+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 2267, "days_push": 45, "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 1396, forks 457 (observed 2026-08-28T04:04:36.693354+00:00)

## What it is
A collection of Jupyter notebooks and slides accompanying a YouTube course on audio signal processing for machine learning. It covers fundamentals of sound, time- and frequency-domain features, Fourier transforms, spectrograms, and feature extraction using Python libraries like librosa and NumPy.

## Use cases
- learn audio signal processing for machine learning
- understand fourier transform and spectrograms with python
- extract audio features like MFCCs for ML models
- find code examples for librosa audio analysis
- study time-domain and frequency-domain audio features
- get course materials for a digital signal processing tutorial

## When to choose
- you want a structured video course with matching runnable notebooks
- you are a beginner needing intuition for DSP concepts before applying ML to audio
- you want MIT-licensed example code for feature extraction with librosa

## When to avoid
- you need a production-ready audio processing library rather than educational code
- you want a maintained software tool with an API instead of course materials
- you need real-time audio processing or streaming audio systems

## Facets
- artifact type: learning-resource
- maturity: active
- function: audio-processing, machine-learning, data-science
- domain: machine-learning, tutorials
- platform: python, cross-platform
- tags: audio-signal-processing, jupyter-notebooks, youtube-course, feature-extraction, fourier-transform, spectrogram, librosa, music-information-retrieval, audio

## Member repositories
- musikalkemist/AudioSignalProcessingForML (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.693354+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 2026-08-30T04:39:20.190174+00:00, confidence not recorded.
  - readme: https://github.com/musikalkemist/AudioSignalProcessingForML (fetched 2026-08-28T04:04:36.693354+00:00, sha caa2709175df)
- Data as of 2026-08-30T08:39:29.467469+00:00.