Velardo-Audio-Signal-Processing-For-ML

In this series, you'll learn how to process audio data and extract relevant audio features for your machine learning applications.

Lectures

This repo contains my notes following Valerio Velardo's "Deep Learning (for Audio) with Python" series playlist.

Repo	YouTube Video	Topics
Lecture 1 Slides	Audio Signal Processing for Machine Learning	learning goals, contents and the prerequisites
Lecture 2 Slides	Sound and Waveforms	learning goals, contents and the prerequisites
Lecture 3 Slides	Intensity, Loudness, and Timbre	learning goals, contents and the prerequisites
Lecture 4 Slides	Understanding Audio Signals for Machine Learning	learning goals, contents and the prerequisites
Lecture 5 Slides	Types of Audio Features for Machine Learning	learning goals, contents and the prerequisites
Lecture 6 Slides	How to Extract Audio Features	learning goals, contents and the prerequisites
Lecture 7 Slides	Understanding Time Domain Audio Features	learning goals, contents and the prerequisites
Lecture 8 Slides	Extracting the amplitude envelope feature from scratch in Python	learning goals, contents and the prerequisites
Lecture 9 Slides	How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio	learning goals, contents and the prerequisites
Lecture 10 Slides	Demystifying the Fourier Transform: The Intuition	learning goals, contents and the prerequisites
Lecture 11 Slides	Complex Numbers for Audio Signal Processing	learning goals, contents and the prerequisites
Lecture 12 Slides	Defining the Fourier Transform with Complex Numbers	learning goals, contents and the prerequisites
Lecture 13 Slides	Discrete Fourier Transform Explained Easily	learning goals, contents and the prerequisites
Lecture 14 Slides	How to Extract the Fourier Transform with Python	learning goals, contents and the prerequisites
Lecture 15 Slides	Short-Time Fourier Transform Explained Easily	learning goals, contents and the prerequisites
Lecture 16 Slides	How to Extract Spectrograms from Audio with Python	learning goals, contents and the prerequisites
Lecture 17 Slides	Mel Spectrograms Explained Easily	learning goals, contents and the prerequisites
Lecture 18 Slides	Extracting Mel Spectrograms with Python	learning goals, contents and the prerequisites
Lecture 19 Slides	Mel-Frequency Cepstral Coefficients Explained Easily	learning goals, contents and the prerequisites
Lecture 20 Slides	Extracting Mel-Frequency Cepstral Coefficients with Python	learning goals, contents and the prerequisites
Lecture 21 Slides	Frequency-Domain Audio Features	learning goals, contents and the prerequisites
Lecture 22 Slides	Implementing Band Energy Ratio in Python from Scratch	learning goals, contents and the prerequisites
Lecture 23 Slides	Extracting Spectral Centroid and Bandwidth with Python and Librosa	learning goals, contents and the prerequisites

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