brief explain about "Audio → Spectrogram → Mel-spectrogram → MFCC"

 Audio → Spectrogram → Mel-spectrogram → MFCC

Spectrogram = raw photo

Mel-spectrogram = photo adjusted for human vision

MFCC = compressed, essential features extracted from that photo

1. Spectrogram

• Raw time-frequency representation
• Shows energy at each frequency over time
• Doesn't account for human perception

2. Mel-spectrogram

• Spectrogram mapped to mel scale
• Mimics human frequency perception
• Still maintains all frequency band information

3. MFCC

• Derived FROM the mel-spectrogram
• Additional step: DCT (Discrete Cosine Transform) is applied
• Keeps only lower coefficients (dimensionality reduction)
• Decorrelates features

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1. Audio → Spectrogram
   • Start with raw audio waveform
   • Apply pre-emphasis to boost higher frequencies
   • Frame the signal into short segments (typically 20-40ms with overlap)
   • Apply window function (usually Hamming) to reduce edge effects
   • Perform FFT on each frame
   • Calculate power spectrum (|FFT|²)
2. Spectrogram → Mel-spectrogram
   • Create mel filter banks (triangular overlapping windows)
   • Convert frequencies to mel scale using formula: mel = 2595 * log10(1 + f/700)
   • Apply mel filter banks to power spectrum
   • Sum up the energy in each mel band
3. Mel-spectrogram → MFCC
   • Take logarithm of mel filter bank energies (to match human perception)
   • Apply Discrete Cosine Transform (DCT)
   • Keep first N coefficients (typically 13-39)
   • Optionally:
     • Calculate delta (velocity) features
     • Calculate delta-delta (acceleration) features
     • Apply cepstral mean normalization (CMN)

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