pyampact.speechDescriptorsUtils.compute_wt_features

pyampact.speechDescriptorsUtils.compute_wt_features(y, wavelet='db4', level=5)[source]

Compute wavelet-based features from a 1-D signal using discrete wavelet transform (DWT).

Features are derived from the normalized energy distribution of wavelet subbands.

Parameters:

  • y (1D numpy array) – input signal

  • wavelet (str) – Wavelet family used for decomposition; default = “db4”

  • level (int) – Decomposition level; default = 5

Returns:

  • wt_entropy (float) – Shannon entropy (base 2) of the normalized wavelet subband energies.

  • wt_low_high_ratio (float) – Ratio of high-frequency subband energy to low-frequency subband energy.