Meta-Learning

Mel-Frequency Cepstral Coefficients (MFCCs) are features used in audio and speech processing that capture the short-term power spectrum of sound, reflecting human auditory perception. They transform audio signals into compact representations by applying a mel-scale filter bank, logarithm, and discrete cosine transform, enabling machine learning models to recognize speech patterns and classify audio with high accuracy.