PWT - Pseudo Wavelet Transform

class audioflux.PWT(num=84, radix2_exp=12, samplate=32000, low_fre=None, high_fre=None, bin_per_octave=12, scale_type=SpectralFilterBankScaleType.OCTAVE, style_type=SpectralFilterBankStyleType.SLANEY, normal_type=SpectralFilterBankNormalType.NONE, is_padding=True)

Pseudo Wavelet Transform (PWT).

Parameters
num: int

Number of frequency bins to generate, starting at low_fre.

radix2_exp: int

fft_length=2**radix2_exp

samplate: int

Sampling rate of the incoming audio.

low_fre: float or None

Lowest frequency.

  • Linear/Linsapce/Mel/Bark/Erb, low_fre>=0. default: 0.0

  • Octave/Log, low_fre>=32.703. default: 32.703(C1)

high_fre: float or None

Highest frequency. Default is 16000(samplate/2).

  • Linear is not provided, it is based on samplate / (2 ** radix2_exp).

  • Octave is not provided, it is based on musical pitch.

bin_per_octave: int

Number of bins per octave.

Only Octave must be provided.

scale_type: SpectralFilterBankScaleType

Spectral filter bank type. It determines the type of spectrogram.

See: type.SpectralFilterBankScaleType

style_type: SpectralFilterBankStyleType

Spectral filter bank style type. It determines the bank type of window.

see: type.SpectralFilterBankStyleType

normal_type: SpectralFilterBankNormalType

Spectral filter normal type. It determines the type of normalization.

Linear is not provided.

See: type.SpectralFilterBankNormalType

is_padding: bool

Whether to use padding.

See also

BFT
NSGT
CWT

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)
>>> # PWT can only input fft_length data
>>> # For radix2_exp=12, then fft_length=4096
>>> audio_arr = audio_arr[..., :4096]

Create PWT object of Octave

>>> from audioflux.type import (SpectralFilterBankScaleType, SpectralFilterBankStyleType,
>>>                             SpectralFilterBankNormalType)
>>> from audioflux.utils import note_to_hz
>>> obj = af.PWT(num=84, radix2_exp=12, samplate=sr,
>>>               low_fre=note_to_hz('C1'), bin_per_octave=12,
>>>               scale_type=SpectralFilterBankScaleType.OCTAVE,
>>>               style_type=SpectralFilterBankStyleType.SLANEY,
>>>               normal_type=SpectralFilterBankNormalType.NONE)

Extract spectrogram

>>> import numpy as np
>>> spec_arr = obj.pwt(audio_arr)
>>> spec_arr = np.abs(spec_arr)

Show spectrogram plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_spec
>>> fig, ax = plt.subplots()
>>> img = fill_spec(spec_arr, axes=ax,
>>>                 x_coords=obj.x_coords(),
>>>                 y_coords=obj.y_coords(),
>>>                 x_axis='time', y_axis='log',
>>>                 title='PWT-Octave Spectrogram')
>>> fig.colorbar(img, ax=ax)
../_images/pwt-1.png

Methods

get_bin_band_arr()

Get bin band array

get_fre_band_arr()

Get an array of frequency bands of different scales.

pwt(data_arr)

Get spectrogram data

x_coords()

Get the X-axis coordinate

y_coords()

Get the Y-axis coordinate
get_fre_band_arr()

Get an array of frequency bands of different scales. Based on the scale_type determination of the initialization.

Returns
out: np.ndarray [shape=(fre, )]
get_bin_band_arr()

Get bin band array

Returns
out: np.ndarray [shape=[n_bin,]]
pwt(data_arr)

Get spectrogram data

Parameters
data_arr: np.ndarray [shape=(…, 2**radix2_exp)]

Audio data array

Returns
out: np.ndarray [shape=(…, fre, time), dtype=np.complex]

The matrix of PWT

y_coords()

Get the Y-axis coordinate

Returns
out: np.ndarray [shape=(fre,)]
x_coords()

Get the X-axis coordinate

Returns
out: np.ndarray [shape=(time,)]