ketanhdoshi.github.io/Audio-Augment
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Audio Deep Learning Made Simple - Data Preparation and Augmentation
This is the third article in my series on audio deep learning. So far we’ve learned how sound is represented digitally, and that deep learning architectures usually use a spectrogram of the sound. We’ve also seen how to pre-process audio data in Python to generate Mel Spectrograms.
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Audio Deep Learning Made Simple - Data Preparation and Augmentation
This is the third article in my series on audio deep learning. So far we’ve learned how sound is represented digitally, and that deep learning architectures usually use a spectrogram of the sound. We’ve also seen how to pre-process audio data in Python to generate Mel Spectrograms.
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https://ketanhdoshi.github.io/Audio-AugmentAudio Deep Learning Made Simple - Data Preparation and Augmentation
This is the third article in my series on audio deep learning. So far we’ve learned how sound is represented digitally, and that deep learning architectures usually use a spectrogram of the sound. We’ve also seen how to pre-process audio data in Python to generate Mel Spectrograms.
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11- titleAudio Deep Learning Made Simple - Data Preparation and Augmentation | Ketan Doshi Blog
- titleAudio Deep Learning Made Simple - Data Preparation and Augmentation | Ketan Doshi Blog
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og:localeen_US- og:descriptionThis is the third article in my series on audio deep learning. So far we’ve learned how sound is represented digitally, and that deep learning architectures usually use a spectrogram of the sound. We’ve also seen how to pre-process audio data in Python to generate Mel Spectrograms.
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15- https://github.com/ketanhdoshi/ml
- https://ketanhdoshi.github.io/Audio-ASR
- https://ketanhdoshi.github.io/Audio-Augment
- https://ketanhdoshi.github.io/Audio-Classification
- https://ketanhdoshi.github.io/Audio-Intro