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Audio Signal Processing for Machine Learning | Audio Signal Processing for Machine Learning 23 videos | ||
23 videos | Master key audio signal processing concepts. Learn how to process raw audio data to power your audio-driven [[Deep Learning]] App | ||
Master key audio signal processing concepts. Learn how to process raw audio data to power your audio-driven | |||
= Audio Signal Processing for Machine Learning = | |||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="iCwMQJnKk2c" /> | |||
= Sound and Waveforms = | |||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="bnHHVo3j124" /> | |||
= Intensity, Loudness, and Timbre = | |||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Jkoysm1fHUw" /> | |||
= Understanding Audio Signals for Machine Learning = | |||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="daB9naGBVv4" /> | |||
= Types of Audio Features for Machine Learning = | |||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ZZ9u1vUtcIA" /> | |||
= How to Extract Audio Features = | |||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="8A-W1xk7qs8" /> | |||
= Understanding Time Domain Audio Features = | = Understanding Time Domain Audio Features = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="SRrQ_v-OOSg" /> | |||
= Extracting the amplitude envelope feature from scratch in Python = | = Extracting the amplitude envelope feature from scratch in Python = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="rlypsap6Wow" /> | |||
= How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio = | = How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio = | ||
EycaSbIRx-0 | |||
= Demystifying the Fourier Transform: The Intuition = | = Demystifying the Fourier Transform: The Intuition = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="XQ45IgG6rJ4" /> | |||
= Complex Numbers for Audio Signal Processing = | = Complex Numbers for Audio Signal Processing = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="F4m0AWCgA" /> | |||
= Defining the Fourier Transform with Complex Numbers = | = Defining the Fourier Transform with Complex Numbers = | ||
KxRmbtJWUzI | |||
= Discrete Fourier Transform Explained Easily = | = Discrete Fourier Transform Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ZUi_jdOyxIQ" /> | |||
= How to Extract the Fourier Transform with Python = | = How to Extract the Fourier Transform with Python = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="R-5uxKTRjzM" /> | |||
= Short-Time Fourier Transform Explained Easily = | = Short-Time Fourier Transform Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="-Yxj3yfvY-4" /> | |||
= How to Extract Spectrograms from Audio with Python = | = How to Extract Spectrograms from Audio with Python = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="3gzI4Z2OFgY" /> | |||
= Mel Spectrograms Explained Easily = | = Mel Spectrograms Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="9GHCiiDLHQ4" /> | |||
= Extracting Mel Spectrograms with Python = | = Extracting Mel Spectrograms with Python = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="TdnVE5m3o_0" /> | |||
= Mel-Frequency Cepstral Coefficients Explained Easily = | = Mel-Frequency Cepstral Coefficients Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="4_SH2nfbQZ8" /> | |||
= Extracting Mel-Frequency Cepstral Coefficients with Python = | = Extracting Mel-Frequency Cepstral Coefficients with Python = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="WJI-17MNpdE" /> | |||
= Frequency-Domain Audio Features = | = Frequency-Domain Audio Features = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="3-bjAoAxQ9o" /> | |||
= Implementing Band Energy Ratio in Python from Scratch = | = Implementing Band Energy Ratio in Python from Scratch = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="8UJ8ZDR7yUs" /> | |||
= Extracting Spectral Centroid and Bandwidth with Python and Librosa = | = Extracting Spectral Centroid and Bandwidth with Python and Librosa = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="j6NTatoi928" /> | |||
Latest revision as of 22:40, 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 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]