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Pleco-Xa

Feature extraction

The feature namespace turns a signal into the compact representations that downstream analysis and machine learning consume: mel spectrograms, MFCCs, chroma, and a family of spectral descriptors. Every function here is fixture-verified — the descriptor math is numerically exact, MFCC lands within 8.7e-5 on the dB scale, and chroma includes tuning estimation.

Like the rest of the library it is arrays in, arrays out: pass a mono Float32Array and a sample rate (or a precomputed spectrogram where a function accepts one).

Key functions

Section titled “Key functions”
  • feature.melspectrogram(y, { sr, n_fft, hop_length, n_mels }) — Slaney-scale mel spectrogram (HTK optional). Slaney is the default; this is the front end for MFCC and PCEN.
  • feature.mfcc(y, { sr, n_mfcc }) — mel-frequency cepstral coefficients: log-power mel → DCT-II → optional liftering. feature.mfccFromLogMel runs the cepstral core directly on a log-mel matrix.
  • feature.chroma_stft(y, { sr, n_fft, hop_length }) — 12-bin pitch-class energy via the Gaussian chroma filterbank, including tuning estimation.
  • feature.spectral_centroid / spectral_bandwidth / spectral_rolloff / spectral_flatness / spectral_contrast — the spectral descriptors, each accepting a signal or a magnitude spectrogram S.
  • feature.rms(y) / feature.zero_crossing_rate(y) — frame-wise energy and zero-crossing rate.
  • feature.pitch_tuning / feature.estimate_tuning — deviation from A440 in fractional bins; feeds chroma.
  • feature.piptrack_peaks / feature.logFrequencySpectrum — parabolic pitch peaks and a log-frequency spectrum (an approximate constant-Q, honestly named — it is not a true CQT).

Example

Section titled “Example”
import { feature } from 'pleco-xa'


// y: mono Float32Array, sr: sample rate
const mel = feature.melspectrogram(y, { sr, n_fft: 2048, hop_length: 512, n_mels: 128 })
const mfcc = feature.mfcc(y, { sr, n_mfcc: 20 })          // [20][n_frames]
const centroid = feature.spectral_centroid(y, { sr })     // brightness, per frame
const chroma = feature.chroma_stft(y, { sr })             // [12][n_frames]

Notes

Section titled “Notes”
  • Slaney mel is the default (HTK is opt-in via htk: true) — the single biggest lever on mel and MFCC values.
  • The MFCC pipeline uses power_to_db (not natural log) and a cached DCT-II basis, following the standard cepstral definition.
  • logFrequencySpectrum is an FFT-bin approximation, not a true constant-Q transform — it is named honestly rather than sold as CQT.
  • MFCC deltas: delta_features (exported top-level) is a width-9 Savitzky-Golay slope on interior frames; edges use clamp replication.

See the API reference for every function’s full signature and per-option defaults.