Audio Signal Processing for Machine Learning: Difference between revisions

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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 [[Deep Learning]] App


= Audio Signal Processing for Machine Learning =
= Audio Signal Processing for Machine Learning =

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]

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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]


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