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Audio preprocessing and feature extraction Part-1
- Feature extraction is crucial in learning, aiming to extract essential features without unnecessary ones for improved accuracy.
- Conversion of audio signals into a numerical format for computers requires audio signal processing techniques like sampling and quantization.
- After digitizing audio, NLP is employed to interpret data, enabling various applications like Voice Assistants and Speech-to-Text Systems.
- Sound waves propagate through a medium, generated by vibrating objects causing air compression and expansion.
- Understanding sound attributes like pitch, intensity, frequency, and amplitude is crucial for sound processing.
- Pitch refers to the perceived high or low nature of sound, which can differ from its frequency.
- Intensity quantifies how loud a sound is and is measured in decibels, reflecting the energy per unit area per unit time.
- Audio signals can be represented in the continuous domain as a function of time, analyzed in time and frequency domains.
- Sampling converts continuous-time signals into discrete-time signals, while quantization maps amplitude to discrete levels.
- Librosa, a popular audio processing library in Python, facilitates various operations like loading and plotting audio files.
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