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="fMqL5vckiU0" /> | |||
iCwMQJnKk2c&list | iCwMQJnKk2c&list | ||
= Sound and Waveforms = | = Sound and Waveforms = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
bnHHVo3j124 | bnHHVo3j124 | ||
= Intensity, Loudness, and Timbre = | = Intensity, Loudness, and Timbre = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
Jkoysm1fHUw | Jkoysm1fHUw | ||
= Understanding Audio Signals for Machine Learning = | = Understanding Audio Signals for Machine Learning = | ||
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daB9naGBVv4 | daB9naGBVv4 | ||
= Types of Audio Features for Machine Learning = | = Types of Audio Features for Machine Learning = | ||
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ZZ9u1vUtcIA | ZZ9u1vUtcIA | ||
= How to Extract Audio Features = | = How to Extract Audio Features = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
8A-W1xk7qs8 | 8A-W1xk7qs8 | ||
= Understanding Time Domain Audio Features = | = Understanding Time Domain Audio Features = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
SRrQ_v-OOSg | SRrQ_v-OOSg | ||
= Extracting the amplitude envelope feature from scratch in Python = | = Extracting the amplitude envelope feature from scratch in Python = | ||
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= Demystifying the Fourier Transform: The Intuition = | = Demystifying the Fourier Transform: The Intuition = | ||
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XQ45IgG6rJ4 | XQ45IgG6rJ4 | ||
= Complex Numbers for Audio Signal Processing = | = Complex Numbers for Audio Signal Processing = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
DgF4m0AWCgA | DgF4m0AWCgA | ||
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= Discrete Fourier Transform Explained Easily = | = Discrete Fourier Transform Explained Easily = | ||
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= How to Extract the Fourier Transform with Python = | = How to Extract the Fourier Transform with Python = | ||
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R-5uxKTRjzM | R-5uxKTRjzM | ||
= Short-Time Fourier Transform Explained Easily = | = Short-Time Fourier Transform Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
-Yxj3yfvY-4 | -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="fMqL5vckiU0" /> | |||
3gzI4Z2OFgY | 3gzI4Z2OFgY | ||
= Mel Spectrograms Explained Easily = | = Mel Spectrograms Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
9GHCiiDLHQ4 | 9GHCiiDLHQ4 | ||
= Extracting Mel Spectrograms with Python = | = Extracting Mel Spectrograms with Python = | ||
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TdnVE5m3o_0 | TdnVE5m3o_0 | ||
= Mel-Frequency Cepstral Coefficients Explained Easily = | = Mel-Frequency Cepstral Coefficients Explained Easily = | ||
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> | |||
4_SH2nfbQZ8 | 4_SH2nfbQZ8 | ||
= Extracting Mel-Frequency Cepstral Coefficients with Python = | = Extracting Mel-Frequency Cepstral Coefficients with Python = | ||
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WJI-17MNpdE | WJI-17MNpdE | ||
= Frequency-Domain Audio Features = | = Frequency-Domain Audio Features = | ||
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3-bjAoAxQ9o | 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="fMqL5vckiU0" /> | |||
8UJ8ZDR7yUs | 8UJ8ZDR7yUs | ||
= Extracting Spectral Centroid and Bandwidth with Python and Librosa = | = Extracting Spectral Centroid and Bandwidth with Python and Librosa = | ||
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j6NTatoi928 | j6NTatoi928 | ||
Revision as of 21:50, 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]
iCwMQJnKk2c&list
Sound and Waveforms[edit]
bnHHVo3j124
Intensity, Loudness, and Timbre[edit]
Jkoysm1fHUw
Understanding Audio Signals for Machine Learning[edit]
daB9naGBVv4
Types of Audio Features for Machine Learning[edit]
ZZ9u1vUtcIA
How to Extract Audio Features[edit]
8A-W1xk7qs8
Understanding Time Domain Audio Features[edit]
SRrQ_v-OOSg
Extracting the amplitude envelope feature from scratch in Python[edit]
rlypsap6Wow
How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio[edit]
EycaSbIRx-0
Demystifying the Fourier Transform: The Intuition[edit]
XQ45IgG6rJ4
Complex Numbers for Audio Signal Processing[edit]
DgF4m0AWCgA
Defining the Fourier Transform with Complex Numbers[edit]
KxRmbtJWUzI
Discrete Fourier Transform Explained Easily[edit]
ZUi_jdOyxIQ
How to Extract the Fourier Transform with Python[edit]
R-5uxKTRjzM
Short-Time Fourier Transform Explained Easily[edit]
-Yxj3yfvY-4
How to Extract Spectrograms from Audio with Python[edit]
3gzI4Z2OFgY
Mel Spectrograms Explained Easily[edit]
9GHCiiDLHQ4
Extracting Mel Spectrograms with Python[edit]
TdnVE5m3o_0
Mel-Frequency Cepstral Coefficients Explained Easily[edit]
4_SH2nfbQZ8
Extracting Mel-Frequency Cepstral Coefficients with Python[edit]
WJI-17MNpdE
Frequency-Domain Audio Features[edit]
3-bjAoAxQ9o
Implementing Band Energy Ratio in Python from Scratch[edit]
8UJ8ZDR7yUs
Extracting Spectral Centroid and Bandwidth with Python and Librosa[edit]
j6NTatoi928