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4 weeks ago

Tutorials

← Older revision Revision as of 21:03, 29 August 2021 Line 30: Line 30: * [[Audio Signal Processing for Machine Learning]] - 23 videos* [[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.  Master key audio signal processing concepts. Learn how to process raw audio data to power your audio-driven AI applications. [https://www.youtube.com/c/ValerioVelardoTheSoundofAI Valerio Velardo - The Sound of AI] * [[Generating Sound with Neural Networks]] - 14 videos Learn how to generate sound from audio files 🎧 🎧 using Variational Autoencoders. [https://www.youtube.com/c/ValerioVelardoTheSoundofAI Valerio Velardo - The Sound of AI][https://www.youtube.com/c/ValerioVelardoTheSoundofAI Valerio Velardo - The Sound of AI]

Techbot

Audio Signal Processing for Machine Learning

4 weeks ago

Audio Signal Processing for Machine Learning

← Older revision Revision as of 20:58, 29 August 2021 (One intermediate revision by the same user not shown)Line 5: Line 5:

= Audio Signal Processing for Machine Learning == Audio Signal Processing for Machine Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="iCwMQJnKk2c" /> iCwMQJnKk2c&list  

= Sound and Waveforms == Sound and Waveforms = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="bnHHVo3j124" /> bnHHVo3j124



= Intensity, Loudness, and Timbre == Intensity, Loudness, and Timbre = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Jkoysm1fHUw" /> Jkoysm1fHUw 

= Understanding Audio Signals for Machine Learning == Understanding Audio Signals for Machine Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="daB9naGBVv4" /> daB9naGBVv4 

= Types of Audio Features for Machine Learning == Types of Audio Features for Machine Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ZZ9u1vUtcIA" /> ZZ9u1vUtcIA 

= How to Extract Audio Features == How to Extract Audio Features = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="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" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="SRrQ_v-OOSg" /> 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="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="rlypsap6Wow" /> 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 = Line 42: Line 39:

= Demystifying the Fourier Transform: The Intuition == Demystifying the Fourier Transform: The Intuition = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="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" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="F4m0AWCgA" /> DgF4m0AWCgA 

= Defining the Fourier Transform with Complex Numbers == Defining the Fourier Transform with Complex Numbers = KxRmbtJWUzIKxRmbtJWUzI

= Discrete Fourier Transform Explained Easily == Discrete Fourier Transform Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ZUi_jdOyxIQ" /> 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="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="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" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="-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" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="3gzI4Z2OFgY" /> 3gzI4Z2OFgY 



= Mel Spectrograms Explained Easily == Mel Spectrograms Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="9GHCiiDLHQ4" /> 9GHCiiDLHQ4 

= Extracting Mel Spectrograms with Python == Extracting Mel Spectrograms with Python = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="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" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="4_SH2nfbQZ8" /> 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="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="WJI-17MNpdE" /> WJI-17MNpdE



= Frequency-Domain Audio Features == Frequency-Domain Audio Features = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="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" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="8UJ8ZDR7yUs" /> 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="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="j6NTatoi928" /> j6NTatoi928
Techbot

Audio Signal Processing for Machine Learning

4 weeks ago

Audio Signal Processing for Machine Learning

← Older revision Revision as of 20:58, 29 August 2021 (2 intermediate revisions by the same user not shown)Line 5: Line 5:

= Audio Signal Processing for Machine Learning == Audio Signal Processing for Machine Learning = iCwMQJnKk2c&list<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="iCwMQJnKk2c" />  

= Sound and Waveforms == Sound and Waveforms = bnHHVo3j124<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="bnHHVo3j124" />



= 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 = daB9naGBVv4<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="daB9naGBVv4" />  

= Types of Audio Features for Machine Learning == Types of Audio Features for Machine Learning = ZZ9u1vUtcIA<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ZZ9u1vUtcIA" />     = How to Extract Audio Features == How to Extract Audio Features = 8A-W1xk7qs8<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="8A-W1xk7qs8" />     = Understanding Time Domain Audio Features == Understanding Time Domain Audio Features = SRrQ_v-OOSg<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 = Line 32: Line 39:

= 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 = DgF4m0AWCgA<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 = KxRmbtJWUzIKxRmbtJWUzI

= Discrete Fourier Transform Explained Easily == Discrete Fourier Transform Explained Easily = ZUi_jdOyxIQ<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 = R-5uxKTRjzM<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 = 3gzI4Z2OFgY<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<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="WJI-17MNpdE" />



= 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" />
Techbot

Audio Signal Processing for Machine Learning

4 weeks ago

← Older revision Revision as of 20:50, 29 August 2021 Line 5: Line 5:

= Audio Signal Processing for Machine Learning == Audio Signal Processing for Machine Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> iCwMQJnKk2c&listiCwMQJnKk2c&list



= Sound and Waveforms == Sound and Waveforms = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> bnHHVo3j124bnHHVo3j124



= Intensity, Loudness, and Timbre == Intensity, Loudness, and Timbre = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> Jkoysm1fHUwJkoysm1fHUw

= Understanding Audio Signals for Machine Learning == Understanding Audio Signals for Machine Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> daB9naGBVv4daB9naGBVv4

= Types of Audio Features for Machine Learning == Types of Audio Features for Machine Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> ZZ9u1vUtcIAZZ9u1vUtcIA = How to Extract Audio Features == How to Extract Audio Features = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> 8A-W1xk7qs88A-W1xk7qs8 = Understanding Time Domain Audio Features == Understanding Time Domain Audio Features = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> SRrQ_v-OOSgSRrQ_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="fMqL5vckiU0" /> rlypsap6Wowrlypsap6Wow

Line 32: Line 42:

= Demystifying the Fourier Transform: The Intuition == Demystifying the Fourier Transform: The Intuition = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> XQ45IgG6rJ4XQ45IgG6rJ4

= Complex Numbers for Audio Signal Processing == Complex Numbers for Audio Signal Processing = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> DgF4m0AWCgADgF4m0AWCgA

Line 41: Line 53:

= Discrete Fourier Transform Explained Easily == Discrete Fourier Transform Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> ZUi_jdOyxIQZUi_jdOyxIQ

Line 46: Line 59:

= 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="fMqL5vckiU0" /> R-5uxKTRjzMR-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" /> 3gzI4Z2OFgY3gzI4Z2OFgY



= Mel Spectrograms Explained Easily == Mel Spectrograms Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> 9GHCiiDLHQ49GHCiiDLHQ4

= Extracting Mel Spectrograms with Python == Extracting Mel Spectrograms with Python = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> TdnVE5m3o_0TdnVE5m3o_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_SH2nfbQZ84_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="fMqL5vckiU0" /> WJI-17MNpdEWJI-17MNpdE



= Frequency-Domain Audio Features == Frequency-Domain Audio Features = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> 3-bjAoAxQ9o3-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" /> 8UJ8ZDR7yUs8UJ8ZDR7yUs

= 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="fMqL5vckiU0" /> j6NTatoi928j6NTatoi928
Techbot

Main Page

4 weeks ago

← Older revision Revision as of 20:46, 29 August 2021 (2 intermediate revisions by the same user not shown)Line 1: Line 1: <strong>Electronic Music Coders Amsterdam</strong><strong>E=MC²³ Electronic Music Coders Amsterdam</strong>

https://www.emc23.com/sites/default/files/images/header-1500x185.png[https://www.emc23.com https://www.emc23.com/sites/default/files/images/header-1500x185.png]

== The Group ==== The Group ==
Techbot

Audio Signal Processing for Machine Learning

4 weeks ago

← Older revision Revision as of 20:36, 29 August 2021 Line 5: Line 5:

= Audio Signal Processing for Machine Learning == Audio Signal Processing for Machine Learning = Sound and WaveformsiCwMQJnKk2c&list Intensity, Loudness, and Timbre  Understanding Audio Signals for Machine Learning  Types of Audio Features for Machine Learning= Sound and Waveforms = How to Extract Audio FeaturesbnHHVo3j124     = Intensity, Loudness, and Timbre = Jkoysm1fHUw   = Understanding Audio Signals for Machine Learning = daB9naGBVv4   = Types of Audio Features for Machine Learning = ZZ9u1vUtcIA = How to Extract Audio Features = 8A-W1xk7qs8 = Understanding Time Domain Audio Features == Understanding Time Domain Audio Features = SRrQ_v-OOSg = Extracting the amplitude envelope feature from scratch in Python == Extracting the amplitude envelope feature from scratch in Python = 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 = XQ45IgG6rJ4 = Complex Numbers for Audio Signal Processing == Complex Numbers for Audio Signal Processing = DgF4m0AWCgA = 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 = ZUi_jdOyxIQ = How to Extract the Fourier Transform with Python == How to Extract the Fourier Transform with Python = R-5uxKTRjzM = Short-Time Fourier Transform Explained Easily == Short-Time Fourier Transform Explained Easily = -Yxj3yfvY-4 = How to Extract Spectrograms from Audio with Python == How to Extract Spectrograms from Audio with Python = 3gzI4Z2OFgY = Mel Spectrograms Explained Easily == Mel Spectrograms Explained Easily = 9GHCiiDLHQ4 = Extracting Mel Spectrograms with Python == Extracting Mel Spectrograms with Python = TdnVE5m3o_0 = Mel-Frequency Cepstral Coefficients Explained Easily == Mel-Frequency Cepstral Coefficients Explained Easily = 4_SH2nfbQZ8 = Extracting Mel-Frequency Cepstral Coefficients with Python == Extracting Mel-Frequency Cepstral Coefficients with Python = WJI-17MNpdE = Frequency-Domain Audio Features == Frequency-Domain Audio Features = 3-bjAoAxQ9o = Implementing Band Energy Ratio in Python from Scratch == Implementing Band Energy Ratio in Python from Scratch = 8UJ8ZDR7yUs = Extracting Spectral Centroid and Bandwidth with Python and Librosa == Extracting Spectral Centroid and Bandwidth with Python and Librosa = j6NTatoi928
Techbot

Audio Signal Processing for Machine Learning

4 weeks ago

Created page with "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 applicat..."

New page

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 =
Sound and Waveforms
Intensity, Loudness, and Timbre
Understanding Audio Signals for Machine Learning
Types of Audio Features for Machine Learning
How to Extract Audio Features
= Understanding Time Domain Audio Features =
= Extracting the amplitude envelope feature from scratch in Python =
= How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio =
= Demystifying the Fourier Transform: The Intuition =
= Complex Numbers for Audio Signal Processing =
= Defining the Fourier Transform with Complex Numbers =
= Discrete Fourier Transform Explained Easily =
= How to Extract the Fourier Transform with Python =
= Short-Time Fourier Transform Explained Easily =
= How to Extract Spectrograms from Audio with Python =
= Mel Spectrograms Explained Easily =
= Extracting Mel Spectrograms with Python =
= Mel-Frequency Cepstral Coefficients Explained Easily =
= Extracting Mel-Frequency Cepstral Coefficients with Python =
= Frequency-Domain Audio Features =
= Implementing Band Energy Ratio in Python from Scratch =
= Extracting Spectral Centroid and Bandwidth with Python and Librosa =
Techbot

Deep Learning

4 weeks ago

Tutorials

← Older revision Revision as of 19:45, 29 August 2021 (2 intermediate revisions by the same user not shown)Line 24: Line 24: Negative predictive value SpecificityNegative predictive value Specificity == Tutorials ==== Tutorials == * [[Deep Learning (for Audio) with Python]]* [[Deep Learning (for Audio) with Python]] - 19 videos In this series, I explore theory and implementation of deep learning in the Python programming language. The course focuses on applications of deep learning for audio and music, but discusses general algorithms and principles applicable to any problem. I use TensorFlow. [https://www.youtube.com/c/ValerioVelardoTheSoundofAI Valerio Velardo - The Sound of AI]   * [[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. [https://www.youtube.com/c/ValerioVelardoTheSoundofAI Valerio Velardo - The Sound of AI]

= Deep learning  architectures used for music generation == Deep learning  architectures used for music generation =
Techbot

Deep Learning (for Audio) with Python

4 weeks ago

← Older revision Revision as of 19:35, 29 August 2021 (3 intermediate revisions by the same user not shown)Line 1: Line 1: = Deep Learning (for Audio) with Python: Course Overview = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> = AI, machine learning and deep learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="1LLxZ35ru_g" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="1LLxZ35ru_g" /> = Implementing an artificial neuron from scratch = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="qxIaW-WvLDU" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="qxIaW-WvLDU" /> = Vector and matrix operations = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="FmD1S5yP_os" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="FmD1S5yP_os" /> = Computation in neural networks = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="QUCzvlgvk6I" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="QUCzvlgvk6I" /> = Implementing a neural network from scratch in Python = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="0oWnheK-gGk" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="0oWnheK-gGk" /> = Training a neural network: Backward propagation and gradient descent = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ScL18goxsSg" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ScL18goxsSg" /> = TRAINING A NEURAL NETWORK: Implementing backpropagation and gradient descent from scratch = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Z97XGNUUx9o" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Z97XGNUUx9o" /> = How to implement a (simple) neural network with TensorFlow 2 = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="JdXxaZcQer8" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="JdXxaZcQer8" /> = Understanding audio data for deep learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="m3XbqfIij_Y" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="m3XbqfIij_Y" /> = Preprocessing audio data for Deep Learning = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Oa_d-zaUti8" /> = Music genre classification: Preparing the dataset = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="szyGiObZymo" /><evlplayer id="player1" w="480" h="360" service="youtube" defaultid="szyGiObZymo" /> = Implementing a neural network for music genre classification = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="_xcFAiufwd0" /> = SOLVING OVERFITTING in neural networks = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="Gf5DO6br0ts" /> = Convolutional Neural Networks Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="t3qWfUYJEYU" /> = How to Implement a CNN for Music Genre Classification = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="dOG-HxpbMSw" /> = Recurrent Neural Networks Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="DY82Goknf0s" /> = Long Short Term Memory (LSTM) Networks Explained Easily = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="eCvz-kB4yko" /> = How to Implement an RNN-LSTM Network for Music Genre Classification = <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="4nXI0h2sq2I" />
Techbot

Main Page

4 weeks ago

Studio Technology

← Older revision Revision as of 17:04, 29 August 2021 (2 intermediate revisions by the same user not shown)Line 2: Line 2:

https://www.emc23.com/sites/default/files/images/header-1500x185.pnghttps://www.emc23.com/sites/default/files/images/header-1500x185.png https://www.meetup.com/Electronic-Music-Coding

== The Group ==== The Group == Line 31: Line 29:

=== [[Studio Technology]] ====== [[Studio Technology]] === Software, Hardwrae, studio design, With a DIY mentality we cover the project, bedroom and livestremaing studios and everything needed to run one.Software, Hardware, Studio design, With a DIY mentality we cover the project, bedroom and livestreaming studios and everything needed to run one.

Enter the [[Studio Technology]] sectionEnter the [[Studio Technology]] section
Techbot

Deep Learning

4 weeks ago

Python

← Older revision Revision as of 17:02, 29 August 2021 Line 23: Line 23:

Negative predictive value SpecificityNegative predictive value Specificity  == Tutorials == [[Deep Learning (for Audio) with Python]]* [[Deep Learning (for Audio) with Python]]

= Deep learning  architectures used for music generation == Deep learning  architectures used for music generation =
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Deep Learning (for Audio) with Python

4 weeks ago

Created page with "<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="fMqL5vckiU0" /> <evlplayer id="player1" w="480" h="360" service="youtube" defaultid="1LLxZ35ru_g" /> <evlp..."

New page

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<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="ScL18goxsSg" />
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Techbot

Deep Learning

4 weeks ago

Python

← Older revision Revision as of 16:56, 29 August 2021 Line 23: Line 23:

Negative predictive value SpecificityNegative predictive value Specificity [[Deep Learning (for Audio) with Python]]

= Deep learning  architectures used for music generation == Deep learning  architectures used for music generation =
Techbot

Composition

4 weeks 1 day ago

← Older revision Revision as of 21:32, 28 August 2021 Line 1: Line 1: == AI [[Deep Learning]] Composition == * [[Automatic Music Generation]] * Death Metal * Techno == DAW Composition ==== DAW Composition == * Rapid Composer* Rapid Composer Line 6: Line 12: == Generative Composition ==== Generative Composition == * Koan* Koan * [[Generative Music]] ===Generative AI and DeepComposer* [[Generative Music]]

===Generative AI and DeepComposer ===

* explore the AWS DeepComposer service.* explore the AWS DeepComposer service. Line 33: Line 40: * Choose how to train your model.* Choose how to train your model.

 == SSEYO Koan Brian Eno used it. Where is it now? == ==SSEYO Koan Brian Eno used it. Where is it now? ==

https://intermorphic.com/sseyo/koan/  https://intermorphic.com/sseyo/koan/   Line 40: Line 46: == Dynamic Composition ==== Dynamic Composition == FMODFMOD == AI [[Deep Learning]] Composition == * [[Automatic Music Generation]] * Death Metal * Techno

== [[Game Music]] ==== [[Game Music]] ==
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Automatic Music Generation

4 weeks 1 day ago

Why and What is a Convolution?

← Older revision Revision as of 21:10, 28 August 2021 Line 136: Line 136: convolutionconvolution

What is 1D Convolution?= 1D Convolution =   The objective of 1D convolution is similar to the Long Short Term Memory model. It is used to solve similar tasks to those of LSTM. In 1D convolution, a kernel or a filter moves along only one direction:The objective of 1D convolution is similar to the Long Short Term Memory model. It is used to solve similar tasks to those of LSTM. In 1D convolution, a kernel or a filter moves along only one direction:

convolution[[File:Calculations-involved-in-a-1D-convolution-operation-300x212.png|thumb|alt=Calculations involved in a 1D convolution operation|Calculations involved in a 1D convolution operation]]

The output of convolution depends upon the size of the kernel, input shape, type of padding, and stride. Now, I will walk you through different types of padding for understanding the importance of using Dilated Causal 1D Convolution layers.The output of convolution depends upon the size of the kernel, input shape, type of padding, and stride. Now, I will walk you through different types of padding for understanding the importance of using Dilated Causal 1D Convolution layers.
Techbot

Automatic Music Generation

4 weeks 1 day ago

Why and What is a Convolution?

← Older revision Revision as of 21:07, 28 August 2021 Line 127: Line 127: For example, in the case of image processing, convolving the image with a filter gives us a feature map.For example, in the case of image processing, convolving the image with a filter gives us a feature map.

music convolving[[File:Conv output.jpg|thumb|alt=In the case of image processing, convolving the image with a filter gives us a feature map|In the case of image processing, convolving the image with a filter gives us a feature map]]

Convolution is a mathematical operation that combines 2 functions. In the case of image processing, convolution is a linear combination of certain parts of an image with the kernel.Convolution is a mathematical operation that combines 2 functions. In the case of image processing, convolution is a linear combination of certain parts of an image with the kernel. Line 209: Line 209:

wavenetwavenet

= The Workflow of WaveNet: == The Workflow of WaveNet: =
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