pyFDN.MatchEnergyDecay#

class pyFDN.MatchEnergyDecay(target, *, window=4096, hop=None, bands=None, floor_db=-45.0)[source]#

RMS dB error of the octave-band energy decay curves against a reference.

The loss that sees the decay – and the one to add when the decay is a trained parameter (a AttenuationFilter in the post_delay hook). A magnitude spectrogram distance is not a substitute for fitting a decay; see the design note.

Each band’s Schroeder curve is normalized to its own value at \(t=0\), so the loss reads the decay and nothing else – level is left to whatever else is in the objective.

The value is in dB, which puts it many orders of magnitude above a spectrogram distance: weight accordingly, and read TrainLog.loss_log (which stores every term unweighted) to see what each term is worth.

Parameters:

  • target (Any) – Reference IR, shape (n_samples,), (n_samples, n_out) or (n_samples, n_out, n_in). Zero-padded or truncated to the model’s nfft.

  • window (int) – STFT window and hop in samples for the band energies. The default 4096 (85 ms at 48 kHz) resolves the 63 Hz octave; shorter windows leave the low bands with too few bins to be worth reading.

  • hop (int | None) – STFT window and hop in samples for the band energies. The default 4096 (85 ms at 48 kHz) resolves the 63 Hz octave; shorter windows leave the low bands with too few bins to be worth reading.

  • bands (Any) – Band edges in Hz; defaults to the octave bands from 44 Hz to 11.3 kHz.

  • floor_db (float) – Only the part of each band’s curve where the target is still above this level is compared. Past it a measurement is reading its own noise floor, and fitting that would fit the microphone.

__init__(target, *, window=4096, hop=None, bands=None, floor_db=-45.0)[source]#

Methods

__init__(target, *[, window, hop, bands, ...])

check(model)

Preflight against the model it will train, before the first step.

terms()

Flatten into (weight, loss) leaves.

Attributes

name

Short label, used as the key in pyFDN.TrainLog.loss_log.
check(model)[source]#

Preflight against the model it will train, before the first step.

Override to reject or warn about a model this loss cannot be fit on – raising here beats a silently useless optimization run. Most losses place no demands on the model, hence the no-op default.

Return type:

None