ketanhdoshi.github.io/Audio-Mel
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Audio Deep Learning Made Simple - Why Mel Spectrograms perform better
This is the second article in my series on audio deep learning. Now that we know how sound is represented digitally, and that we need to convert it into a spectrogram for use in deep learning architectures, let us understand in more detail how that is done and how we can tune that conversion to get better performance.
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Audio Deep Learning Made Simple - Why Mel Spectrograms perform better
This is the second article in my series on audio deep learning. Now that we know how sound is represented digitally, and that we need to convert it into a spectrogram for use in deep learning architectures, let us understand in more detail how that is done and how we can tune that conversion to get better performance.
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https://ketanhdoshi.github.io/Audio-MelAudio Deep Learning Made Simple - Why Mel Spectrograms perform better
This is the second article in my series on audio deep learning. Now that we know how sound is represented digitally, and that we need to convert it into a spectrogram for use in deep learning architectures, let us understand in more detail how that is done and how we can tune that conversion to get better performance.
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11- titleAudio Deep Learning Made Simple - Why Mel Spectrograms perform better | Ketan Doshi Blog
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og:localeen_US- og:descriptionThis is the second article in my series on audio deep learning. Now that we know how sound is represented digitally, and that we need to convert it into a spectrogram for use in deep learning architectures, let us understand in more detail how that is done and how we can tune that conversion to get better performance.
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