MFCC (Mel-Frequency Cepstral Coefficients)
Mel-Frequency Cepstral Coefficients · Also known as: mel-cepstral features, MFCC features, mel-frequency features
Mel-Frequency Cepstral Coefficients (MFCCs) are a compact representation of audio features that mimic human auditory perception. Introduced by Davis and Mermelstein in 1980, MFCCs are the de facto feature extraction method for speech recognition and environmental sound analysis. They compress the frequency information of audio signals into a small set of coefficients that capture phonetic content while discarding irrelevant details.
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When to use it
Use MFCCs for speech recognition, speaker identification, emotion detection, and environmental sound classification. They are industry-standard for automatic speech recognition (ASR) systems and deep learning models. Apply to audio signals with clear speech or environmental structure. Avoid for music analysis without modification (music requires different features like spectral flux or chroma).
Strengths & limitations
- Compact representation (~13 coefficients per frame) reduces computational cost and storage
- Aligned with human auditory perception; captures perceptually important information
- Robust to speaker variability and moderate noise
- Widely implemented in speech recognition frameworks (HTK, Kaldi, TensorFlow)
- Discards phase information; cannot reconstruct audio from MFCCs alone
- Assumes stationarity within frames; short-term features miss longer-term patterns (overcome with delta/acceleration)
- Not optimal for music; music features (spectral centroid, chroma, etc.) are often better
- Sensitive to microphone characteristics and background noise (requires normalization)
Frequently asked
Why use the mel scale instead of Hz?
Human hearing perceives pitch logarithmically, not linearly. The mel scale reflects this: 1 mel corresponds to 1 just-noticeable change in pitch. Grouping frequencies by mel scale better captures perceptual differences.
What are delta and acceleration coefficients?
Delta coefficients capture the rate of change of MFCCs over time; acceleration coefficients capture the second derivative. Adding these to the static MFCCs significantly improves speech recognition (feature vector ~39 numbers instead of 13).
How do I choose the number of MFCC coefficients?
For speech, 12-13 static coefficients are standard. Including deltas and accelerations, use 39-40 total (13 × 3). For different domains or sample rates, experiment; more coefficients capture more detail but can overfit.
Sources
- Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357-366. DOI: 10.1109/TASSP.1980.1163420 ↗
- Young, S. J., Evermann, G., Gales, M. J., et al. (1996). The HTK Book. Cambridge University Engineering Department. link ↗
- Moustakides, G. V., & Rougui, J. A. (2004). Optimal filtering for polynomial signal models. IEEE Transactions on Signal Processing, 52(8), 2219-2230. link ↗
How to cite this page
ScholarGate. (2026, June 3). Mel-Frequency Cepstral Coefficients. ScholarGate. https://scholargate.app/en/applied-physics/mfcc
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