Filter banks & windows

The filters namespace holds the reusable building blocks that the feature layer composes: the mel filterbank, the chroma filterbank, and the analysis windows. If feature is the “what,” filters is the “how” — the matrices and window functions you can pull out and apply yourself when building a custom pipeline.

Key functions

• filters.mel_filterbank({ sr, n_fft, n_mels, htk, norm }) — the mel projection matrix: continuous Slaney triangular ramps over the FFT frequency grid with Slaney area normalization (HTK scale and other norms optional). Multiply it against a power spectrogram to get a mel spectrogram.
• filters.chroma({ sr, n_fft }) — the chroma filterbank: Gaussian pitch-class bumps with octave weighting and tuning. feature.chroma_stft is a matmul of this against the energy spectrum.
• filters.get_window(type, n) — a window function (hann, hamming, blackman). Periodic by convention (fftbins=True), matching scipy — the correct choice for spectral analysis.

Example

import { filters, stft } from 'pleco-xa'

// build the mel matrix once, reuse across frames
const melFb = filters.mel_filterbank({ sr: 22050, n_fft: 2048, n_mels: 128 })

// a periodic Hann window for your own STFT framing
const win = filters.get_window('hann', 2048)

Notes

• Windows are periodic (2π·i / n), not symmetric (/(n-1)). This is the scipy fftbins=True convention; the symmetric form skews every spectrogram bin.
• The mel filterbank defaults to the Slaney scale with area normalization. Pass htk: true for the HTK scale.
• These are the same filterbanks feature.melspectrogram and feature.chroma_stft use internally; exposing them lets you build custom feature stacks without re-deriving the matrices.

See the API reference for full signatures.