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musikalkemist

AudioSignalProcessingForML

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

TutorialsML/AI fundamentalsJupyter Notebook
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创建于 2020-06-18 · 更新于 2026-10-09 · 今日第 10390 名
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musikalkemistMLsoundGuillermoOvejeroSanchez

musikalkemist/AudioSignalProcessingForML 排名第 10,390，有 1,419 个星标。

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musikalkemist/AudioSignalProcessingForML 排名第 10,390，此处有 8 个同类条目；fmind/mlops-python-package 排名第 10,389。

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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Tutorials
子类别ML/AI fundamentals
星标数1,419
分叉数458
贡献者3
创建日期2020-06-18
更新日期2026-10-09
1 天星标数0
7 天星标数0
排名10,390
上次排名10,389
排名变化-1
7 天星标排名12,606
上次 7 天星标排名8,108
7 天星标排名变化-4,498
总星标排名8,463
上次总星标排名8,447
总星标排名变化-16
国家Spain
README 摘录

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.

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记录了多少星标和分叉？

musikalkemist/AudioSignalProcessingForML 有 1,419 个星标和 458 个分叉。

仓库排名是多少？

musikalkemist/AudioSignalProcessingForML 排名第 10,390。

记录的语言和类别是什么？

musikalkemist/AudioSignalProcessingForML 有 1,419 个星标，语言为 Jupyter Notebook，类别为 Tutorials。

哪个仓库排名最接近？

musikalkemist/AudioSignalProcessingForML 与 fmind/mlops-python-package 比较，后者排名第 10,389，有 1,419 个星标。

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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