Completed
Audio Signal Processing for Machine Learning
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Audio Signal Processing for Machine Learning
Automatically move to the next video in the Classroom when playback concludes
- 1 Audio Signal Processing for Machine Learning
- 2 Sound and Waveforms
- 3 Intensity, Loudness, and Timbre
- 4 Understanding Audio Signals for Machine Learning
- 5 Types of Audio Features for Machine Learning
- 6 How to Extract Audio Features
- 7 Understanding Time Domain Audio Features
- 8 Extracting the amplitude envelope feature from scratch in Python
- 9 How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio
- 10 Demystifying the Fourier Transform: The Intuition
- 11 Complex Numbers for Audio Signal Processing
- 12 Defining the Fourier Transform with Complex Numbers
- 13 Discrete Fourier Transform Explained Easily
- 14 How to Extract the Fourier Transform with Python
- 15 Short-Time Fourier Transform Explained Easily
- 16 How to Extract Spectrograms from Audio with Python
- 17 Mel Spectrograms Explained Easily
- 18 Extracting Mel Spectrograms with Python
- 19 Mel-Frequency Cepstral Coefficients Explained Easily
- 20 Extracting Mel-Frequency Cepstral Coefficients with Python
- 21 Frequency-Domain Audio Features
- 22 Implementing Band Energy Ratio in Python from Scratch
- 23 Extracting Spectral Centroid and Bandwidth with Python and Librosa
