Mel Spectrogram
A spectrogram where frequencies are warped to the mel scale - which better matches human hearing than linear frequency.
Think of it like
A piano keyboard: low keys are spaced widely (low frequencies), high keys are spaced closely (we hear pitch logarithmically).
Example
Raw spectrogram: 0-22050 Hz linearly. Mel spectrogram: frequencies warped so differences we actually perceive are evenly spaced.
How it actually works
Humans perceive pitch logarithmically, not linearly. Mel scale (0-2595 mels) mimics this. Transform: take STFT magnitude, apply triangular filterbank on mel scale, take log (perceptual weighting). Smaller, more interpretable than raw spectrograms. Used as input to speech recognition, music tagging, audio classification. Variants: MFCC (adds DCT, compresses further). Provides good frequency resolution for speech and music.
For product teams
Standard input representation for audio models - improves performance and reduces data size.
For engineers
Convert waveform -> STFT -> power spectrum -> mel-scale filterbank -> log compression. Libraries: librosa, scipy.
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