pyampact.speechDescriptorsUtils.compute_tqwt_features

pyampact.speechDescriptorsUtils.compute_tqwt_features(y, sr, Q=1.0, r=3.0, J=8, n_keep=5)[source]

Compute a fixed-width feature vector from the Tunable Q-factor Wavelet Transform (TQWT). Features are derived from subband energies of the TQWT decomposition of a 1-D signal.

Parameters:

  • y (1D numpy array) – input signal

  • sr (float) – sample rate of the signal in Hz. (Currently unused; included for API consistency.)

  • Q (float) – Q-factor controlling oscillatory behavior of the wavelets; default = 1.0

  • r (float) – redundancy factor of the TQWT; default = 3.0

  • J (int) – number of TQWT decomposition levels; default = 8

  • n_keep (int) – number of lowest-index subband log-energies to return; default = 5

Returns:

features – Feature vector containing, in order:

  • tqwt_e1 .. tqwt_e{n_keep}float

    Log10 subband energies of the first n_keep TQWT bands.

  • tqwt_entropyfloat

    Normalized Shannon entropy of the subband energy distribution.

  • tqwt_centroidfloat

    Energy-weighted centroid of subband indices.

  • tqwt_low_high_ratiofloat

    Ratio of summed low-band energy to high-band energy.

If insufficient valid data are available, all features are returned as NaN.

Return type:

list of float