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musikalkemist

AudioSignalProcessingForML

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

TutorialsML/AI fundamentalsJupyter Notebook
Open on GitHub
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Created 2020-06-18 · Updated 2026-10-09 · #10390 today
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musikalkemistMLsoundGuillermoOvejeroSanchez

musikalkemist/AudioSignalProcessingForML is ranked 10,390 with 1,419 stars.

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musikalkemist/AudioSignalProcessingForML is ranked 10,390 among these 8 peers; fmind/mlops-python-package is ranked 10,389.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageJupyter Notebook
CategoryTutorials
SubcategoryML/AI fundamentals
Stars1,419
Forks458
Contributors3
Created date2020-06-18
Updated date2026-10-09
Stars in 1 day0
Stars in 7 days0
Rank10,390
Previous rank10,389
Rank change-1
7-day stars rank12,606
Previous 7-day stars rank8,108
7-day stars rank change-4,498
Total stars rank8,463
Previous total stars rank8,447
Total stars rank change-16
CountrySpain
README summary

AudioSignalProcessingForML Code and slides of my YouTube series called "Audio Signal Proessing for Machine Learning" This repository is a comprehensive collection of resources, code, and explanations for understanding and implementing audio signal processing techniques, with a focus on applications in machine learning. It serves as a learning guide, starting from the fundamentals of sound and waveforms and progressing to advanced feature extraction methods. Note on Versioning While this v2 release is fully functional and optimized for current environments, it may

differ from the original version shown in the course. The codebase has been updated to reflect modern best practices and improved dependency management. Consequently, the original course version has been deprecated; however, it remains available in the legacy branch for those wishing to follow the video content exactly. Course Structure Foundational Concepts 1. **Sound and waveforms:** _Video | Slides_ 3. **Intensity, loudness, and timbre:** _Video | Slides | Notebook_ 4. **Types of audio features for ML:** _Video | Slides_ 6. **How to extract audio

features:** _Video | Slides_ 7. **Implementing the amplitude envelope:** _Video | Notebook_ 9. **Fourier Transform: The Intuition:** _Video | Slides_ 11. **Complex numbers for audio signal processing:** _Video | Slides_ 12. **Defining the Fourier transform using complex numbers:** _Video | Slides | Notebook_ 13. **Extracting the Discrete Fourier Transform:** _Video | Notebook_ 15. **Short-Time Fourier Transform explained easily:** _Video | Slides_ 16. **Extracting Spectrograms from Audio with Python:** _Video | Notebook_ 17. **Mel Spectrogram Explained Easily:** _Video | Slides_ 18. **Extracting Mel Spectrograms with Python:** _Video | Notebook_ 19. **MFCCs

Explained Easily:** _Video | Slides_ 20. **Extracting MFCCs with Python:** _Video | Notebook_ 21. **Frequency-Domain Audio Features:** _Video | Slides_ 22. **Implementing Band Energy Ratio from Scratch with Python:** _Video | Notebook_ 23. wav files used for the examples in the notebooks.

Frequently asked questions

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musikalkemist/AudioSignalProcessingForML has 1,419 stars and 458 forks.

What is the repository rank?

musikalkemist/AudioSignalProcessingForML is ranked 10,390.

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musikalkemist/AudioSignalProcessingForML has 1,419 stars, uses Jupyter Notebook and belongs to Tutorials.

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musikalkemist/AudioSignalProcessingForML is compared with fmind/mlops-python-package, ranked 10,389 with 1,419 stars.

README

AudioSignalProcessingForML

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

This repository is a comprehensive collection of resources, code, and explanations for understanding and implementing audio signal processing techniques, with a focus on applications in machine learning. It serves as a learning guide, starting from the fundamentals of sound and waveforms and progressing to advanced feature extraction methods.

Note on Versioning

While this v2 release is fully functional and optimized for current environments, it may differ from the original version shown in the course. The codebase has been updated to reflect modern best practices and improved dependency management. Consequently, the original course version has been deprecated; however, it remains available in the legacy branch for those wishing to follow the video content exactly.

Course Structure

Foundational Concepts

  1. Overview: Video | Slides
  2. Sound and waveforms: Video | Slides
  3. Intensity, loudness, and timbre: Video | Slides | Notebook
  4. Understanding audio signals: Video | Slides

Feature Extraction Theory

  1. Types of audio features for ML: Video | Slides
  2. How to extract audio features: Video | Slides
  3. Time-domain audio features: Video | Slides

Time-Domain Implementation

  1. Implementing the amplitude envelope: Video | Notebook
  2. RMS energy and zero-crossing rate: Video | Notebook

Frequency-Domain Concepts

  1. Fourier Transform: The Intuition: Video | Slides
  2. Complex numbers for audio signal processing: Video | Slides
  3. Defining the Fourier transform using complex numbers: Video | Slides | Notebook
  4. Discrete Fourier Transform: Video | Slides

Frequency-Domain Implementation

  1. Extracting the Discrete Fourier Transform: Video | Notebook
  2. Short-Time Fourier Transform explained easily: Video | Slides
  3. Extracting Spectrograms from Audio with Python: Video | Notebook
  4. Mel Spectrogram Explained Easily: Video | Slides
  5. Extracting Mel Spectrograms with Python: Video | Notebook
  6. MFCCs Explained Easily: Video | Slides
  7. Extracting MFCCs with Python: Video | Notebook
  8. Frequency-Domain Audio Features: Video | Slides
  9. Implementing Band Energy Ratio from Scratch with Python: Video | Notebook
  10. Spectral centroid and bandwidth: Video | Notebook

Audio examples

  • audio_resources/: A collection of .wav files used for the examples in the notebooks.
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