CQT

class audioflux.CQT(num=84, samplate=32000, low_fre=32.70319566257483, bin_per_octave=12, factor=1.0, beta=0.0, thresh=0.01, window_type=WindowType.HANN, slide_length=None, normal_type=SpectralFilterBankNormalType.AREA, is_scale=True)

Constant-Q transform (CQT)

Parameters

num: int

Number of frequency bins to generate, starting at low_fre.

Usually: num = octave * bin_per_octave, default: 84 (7 * 12)

samplate: int:

Sampling rate of the incoming audio.

low_fre: float

Lowest frequency. default: 32.703(C1)

bin_per_octave: int

Number of bins per octave.

factor: float

Factor value

beta: float

Beta value

thresh: float

Thresh value

window_type: WindowType

Window type for each frame.

See: type.WindowType

slide_length: int or None

Window sliding length.

normal_type: SpectralFilterBankNormalType

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

See: type.SpectralFilterBankNormalType

is_scale: bool

Whether to use scale.

See also

ST

FST

DWT

WPT

SWT

Examples

Read 220Hz audio data

>>> import audioflux as af
>>> audio_path = af.utils.sample_path('220')
>>> audio_arr, sr = af.read(audio_path)

Create CQT object

>>> from audioflux.type import SpectralFilterBankNormalType
>>> from audioflux.utils import note_to_hz
>>> obj = af.CQT(num=84, samplate=sr, low_fre=note_to_hz('C1'), bin_per_octave=12,
>>>              slide_length=1024, normal_type=SpectralFilterBankNormalType.AREA)

Extract CQT spectrogram

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

Show CQT spectrogram plot

>>> import matplotlib.pyplot as plt
>>> from audioflux.display import fill_spec
>>> audio_len = audio_arr.shape[-1]
>>> fig, ax = plt.subplots()
>>> img = fill_spec(spec_mag_arr, axes=ax,
>>>           x_coords=obj.x_coords(audio_len),
>>>           y_coords=obj.y_coords(),
>>>           x_axis='time', y_axis='log',
>>>           title='CQT Spectrogram')
>>> fig.colorbar(img, ax=ax)

Extract Chroma-cqt data

>>> chroma_arr = obj.chroma(spec_arr, chroma_num=12)

Show Chroma-CQT spectrogram plot

>>> fig, ax = plt.subplots()
>>> img = fill_spec(chroma_arr, axes=ax,
>>>           x_coords=obj.x_coords(audio_len),
>>>           x_axis='time', y_axis='chroma',
>>>           title='Chroma-CQT Spectrogram')
>>> fig.colorbar(img, ax=ax)

Methods

cal_time_length(data_length)	Calculate the length of a frame from audio data.
chroma(m_cqt_data[, chroma_num, data_type, ...])	Calculate the chroma matrix of CQT
cqcc(m_data_arr[, cc_num, rectify_type])	Compute the spectral cqcc feature.
cqhc(m_data_arr[, hc_num])	Compute the spectral cqhc feature.
cqt(data_arr)	Get spectrogram data
deconv(m_data_arr)	Compute the spectral deconv feature.
get_fft_length()	Get fft_length
get_fre_band_arr()	Get an array of frequency bands of CQT scales.
set_scale([flag])	Set scale
x_coords(data_length)	Get the X-axis coordinate
y_coords()	Get the Y-axis coordinate of CQT