Speech Sampling in Audio Signal Processing

The FFT (or Fast Fourier Transform) shape of human voice recordings plays a central role in audio recognition, acting as a bridge between the time and frequency domains of sound. When we speak, our voice produces complex waveforms that vary over time. While these waveforms contain valuable information, it's often the frequency components that are more meaningful. The FFT process breaks these waveforms down into their individual frequencies, creating a spectrogram. This spectrogram is a visual representation of the different frequencies present in the audio at any given time. For deep learning models, this transformation is critical. Just as we convert images into numerical formats for image recognition, the FFT spectrogram converts audio signals into a format that neural networks can understand and process.

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