WPT - Wave Packet Transform

class audioflux.WPT(num=None, radix2_exp=12, samplate=32000, wavelet_type=WaveletDiscreteType.SYM, t1=4, t2=0)

Wave Packet Transform (WPT)

Parameters

num: int or None

Number of frequency bins is 2**num

If num is None, then num = radix2_exp - 1

radix2_exp: int

fft_length=2**radix2_exp

wavelet_type: WaveletDiscreteType

Wavelet discrete type

See: type.WaveletDiscreteType

Note

t1/t2 settings for wavelet_type:

• DB: t1

  • 2~10/20/30/40

• SYM: t1

  • 2~10/20/30

• COIF: t1

  • 1/2/3/4/5

• FK: t1

  • 4/6/8/14/18/22

• BIOR/DMEY: t1.t2

  • 1.1/1.3/1.5

  • 2.2/2.4/2.6/2.8

  • 3.1/3.3/3.5/3.7/3.9

  • 4.4/5.5/6.8

t1: int

t1 value

t2: int

t2 value

See also

CQT

ST

FST

DWT

SWT

Examples

Read 880Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('880')
>>> audio_arr, sr = af.read(audio_path)
>>> audio_arr = audio_arr[..., :4096]

Create WPT object

>>> from audioflux.type import WaveletDiscreteType
>>> obj = af.WPT(num=7, radix2_exp=12, samplate=sr,
>>>              wavelet_type=WaveletDiscreteType.SYM,
>>>              t1=4, t2=0)

Extract WPT data

>>> import numpy as np
>>> coef_arr, m_data_arr = obj.wpt(audio_arr)
>>> m_data_arr = np.abs(m_data_arr)

Show plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_spec, fill_plot
>>> fig, ax = plt.subplots(nrows=2)
>>> fill_spec(m_data_arr, axes=ax[0],
>>>           x_coords=obj.x_coords(), y_coords=obj.y_coords(),
>>>           x_axis='time', y_axis='log',
>>>           title='WPT')
>>> fill_plot(np.arange(coef_arr.shape[-1]), coef_arr,
>>>           axes=ax[1], label='WPT-coef')

Methods

wpt(data_arr)	Get wpt matrix
x_coords()	Get the X-axis coordinate
y_coords()	Get the Y-axis coordinate

get_fre_band_arr

wpt(data_arr)

Get wpt matrix

Parameters

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

Input audio data

Returns

coef_arr: np.ndarray [shape=(…, time), dtype=np.float32]

m_data_arr: np.ndarray [shape=(…, fre, time), dtype=np.float32]

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,)]