Audio Signal Processing for Machine Learning

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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]

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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]