How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio

How to Extract Root-Mean Square Energy and Zero-Crossing Rate from Audio

Explore the extraction of Root-Mean Square Energy (RMSE) and Zero-Crossing Rate (ZCR) from audio data in this comprehensive 32-minute tutorial video. Utilize the Python library librosa to implement these audio feature extraction techniques. Gain insights into how RMS and ZCR values vary across different music genres and audio sources, such as voice and noise. Follow along with a practical demonstration and access the provided code on GitHub to enhance your understanding of audio signal processing for machine learning applications.