Audio Signal Processing for Machine Learning: Difference between revisions
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= Audio Signal Processing for Machine Learning = | = Audio Signal Processing for Machine Learning = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="iCwMQJnKk2c | <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="iCwMQJnKk2c" /> | ||
= Sound and Waveforms = | = Sound and Waveforms = | ||
Revision as of 22:58, 29 August 2021
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 AI applications.
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]
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Demystifying the Fourier Transform: The Intuition[edit]
Complex Numbers for Audio Signal Processing[edit]
Defining the Fourier Transform with Complex Numbers[edit]
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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]