# Audio Feature Vectors and Matching

> Audio feature engineering for matching, spanning time-domain measures, spectral features, MFCCs, pitch and learned speaker/audio embeddings.

- Author: Muhammet Ali Köker
- Language: en
- Canonical: https://alikoker.com.tr/en/audio-feature-vectors-and-matching
- Translation: https://alikoker.com.tr/seste-oznitelik
- Published: 2022-04-24T10:34:23+03:00
- Modified: 2026-08-31T22:21:00+03:00
- Verified: 2026-08-31T22:07:00+03:00
- Type: article

Audio features transform raw waveforms into representations that are more stable for matching, classification and recognition. Depending on the task, systems may use time-domain features, spectral representations or learned embeddings.

## Time and frequency features

Short-time energy and zero-crossing rate are inexpensive time-domain measures. STFT computes frequency components within short windows and produces a time-frequency representation. Window length creates a direct trade-off between temporal and frequency resolution.

## MFCC and spectral representations

[MFCC](/en/wiki/mel-frequency-cepstral-coefficients) is derived from log Mel-filterbank energies followed by a cepstral transform. It has long been a standard feature for speech recognition and speaker modelling.

Pitch, formants, spectral slope, jitter and shimmer represent different physical or perceptual properties of the signal.

## Learned embeddings

Modern systems can map an audio segment directly to a dense [speaker embedding](/en/wiki/speaker-embedding) or a more general audio embedding. Matching can use cosine similarity, PLDA or learned scoring models.

Channel, microphone, codec and acoustic environment can change the representation distribution, so normalisation and domain adaptation belong in the feature pipeline.

At scale, vector dimension also determines storage cost, index size and query latency.

## Cite This Work

Köker, M. A. (2022). Audio Feature Vectors and Matching. alikoker.com.tr. https://alikoker.com.tr/en/audio-feature-vectors-and-matching

- BibTeX: https://alikoker.com.tr/en/audio-feature-vectors-and-matching.bib
- RIS: https://alikoker.com.tr/en/audio-feature-vectors-and-matching.ris
- CSL-JSON: https://alikoker.com.tr/en/audio-feature-vectors-and-matching.csl.json