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 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 AI applications.
 


= Audio Signal Processing for Machine Learning =
= Audio Signal Processing for Machine Learning =
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= Sound and Waveforms =
= Sound and Waveforms =
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= Intensity, Loudness, and Timbre =
= Intensity, Loudness, and Timbre =
Jkoysm1fHUw
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Jkoysm1fHUw" />
 


= Understanding Audio Signals for Machine Learning =
= Understanding Audio Signals for Machine Learning =
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= Types of Audio Features for Machine Learning =
= Types of Audio Features for Machine Learning =
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= How to Extract Audio Features =
= How to Extract Audio Features =
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<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="8A-W1xk7qs8" />
 
 
= Understanding Time Domain Audio Features =
= Understanding Time Domain Audio Features =
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<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 =
rlypsap6Wow
<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 =
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= Demystifying the Fourier Transform: The Intuition =
= Demystifying the Fourier Transform: The Intuition =
XQ45IgG6rJ4
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="XQ45IgG6rJ4" />
 


= Complex Numbers for Audio Signal Processing =
= Complex Numbers for Audio Signal Processing =
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<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
KxRmbtJWUzI


= Discrete Fourier Transform Explained Easily =
= Discrete Fourier Transform Explained Easily =
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<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 =
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<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="R-5uxKTRjzM" />




= Short-Time Fourier Transform Explained Easily =
= Short-Time Fourier Transform Explained Easily =
-Yxj3yfvY-4
<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 =
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<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="3gzI4Z2OFgY" />
 




= Mel Spectrograms Explained Easily =
= Mel Spectrograms Explained Easily =
9GHCiiDLHQ4
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="9GHCiiDLHQ4" />
 


= Extracting Mel Spectrograms with Python =
= Extracting Mel Spectrograms with Python =
TdnVE5m3o_0
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="TdnVE5m3o_0" />
 


= Mel-Frequency Cepstral Coefficients Explained Easily =
= Mel-Frequency Cepstral Coefficients Explained Easily =
4_SH2nfbQZ8
<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 =
WJI-17MNpdE
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= Frequency-Domain Audio Features =
= Frequency-Domain Audio Features =
3-bjAoAxQ9o
<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 =
8UJ8ZDR7yUs
<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 =
j6NTatoi928
<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]


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