librosa.core.pseudo_cqt

librosa.core.pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84, bins_per_octave=12, tuning=0.0, filter_scale=1, norm=1, sparsity=0.01, window=’hann’, scale=True, pad_mode=’reflect’)[source]

Compute the pseudo constant-Q transform of an audio signal.

This uses a single fft size that is the smallest power of 2 that is greater than or equal to the max of:

  1. The longest CQT filter
  2. 2x the hop_length
Parameters:
y : np.ndarray [shape=(n,)]

audio time series

sr : number > 0 [scalar]

sampling rate of y

hop_length : int > 0 [scalar]

number of samples between successive CQT columns.

fmin : float > 0 [scalar]

Minimum frequency. Defaults to C1 ~= 32.70 Hz

n_bins : int > 0 [scalar]

Number of frequency bins, starting at fmin

bins_per_octave : int > 0 [scalar]

Number of bins per octave

tuning : None or float in [-0.5, 0.5)

Tuning offset in fractions of a bin (cents).

If None, tuning will be automatically estimated from the signal.

filter_scale : float > 0

Filter filter_scale factor. Larger values use longer windows.

sparsity : float in [0, 1)

Sparsify the CQT basis by discarding up to sparsity fraction of the energy in each basis.

Set sparsity=0 to disable sparsification.

window : str, tuple, number, or function

Window specification for the basis filters. See filters.get_window for details.

pad_mode : string

Padding mode for centered frame analysis.

See also: librosa.core.stft and np.pad.
Returns:
CQT : np.ndarray [shape=(n_bins, t), dtype=np.float]

Pseudo Constant-Q energy for each frequency at each time.
Raises:
ParameterError

If hop_length is not an integer multiple of 2**(n_bins / bins_per_octave)

Or if y is too short to support the frequency range of the CQT.

Notes

This function caches at level 20.