Audio Signal Processing for Machine Learning

From EMC Electronic Music Coders
Jump to navigation Jump to search

Audio Signal Processing for Machine Learning 23 videos Master key audio signal processing concepts. Learn how to process raw audio data to power your audio-driven Deep Learning App

Audio Signal Processing for Machine Learning[edit]

Sound and Waveforms[edit]


Intensity, Loudness, and Timbre[edit]


Understanding Audio Signals for Machine Learning[edit]


Types of Audio Features for Machine Learning[edit]


How to Extract Audio Features[edit]


Understanding Time Domain Audio Features[edit]


Extracting the amplitude envelope feature from scratch in Python[edit]


How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio[edit]

EycaSbIRx-0

Demystifying the Fourier Transform: The Intuition[edit]


Complex Numbers for Audio Signal Processing[edit]


Defining the Fourier Transform with Complex Numbers[edit]

KxRmbtJWUzI


Discrete Fourier Transform Explained Easily[edit]


How to Extract the Fourier Transform with Python[edit]


Short-Time Fourier Transform Explained Easily[edit]


How to Extract Spectrograms from Audio with Python[edit]


Mel Spectrograms Explained Easily[edit]


Extracting Mel Spectrograms with Python[edit]


Mel-Frequency Cepstral Coefficients Explained Easily[edit]


Extracting Mel-Frequency Cepstral Coefficients with Python[edit]


Frequency-Domain Audio Features[edit]


Implementing Band Energy Ratio in Python from Scratch[edit]


Extracting Spectral Centroid and Bandwidth with Python and Librosa[edit]