Extract cqt/chroma_cqt

Simple extraction of cqt and chroma_cqt

import numpy as np
import audioflux as af

# Get a 220Hz's audio file path
sample_path = af.utils.sample_path('220')

# Read audio data and sample rate
audio_arr, sr = af.read(sample_path)

# Extract CQT
cqt_arr, _ = af.cqt(audio_arr, samplate=sr)

# Extract CQT_CHROMA
chroma_cqt_arr = af.chroma_cqt(audio_arr, samplate=sr)

We recommend using the CQT class, you can use it more flexibly and efficiently.

import numpy as np
import audioflux as af

# Get a 220Hz's audio file path
sample_path = af.utils.sample_path('220')

# Read audio data and sample rate
audio_arr, sr = af.read(sample_path)

# Create CQT object
cqt_obj = af.CQT(num=84, samplate=sr)

# Extract CQT and Chroma_cqt
cqt_arr = cqt_obj.cqt(audio_arr)
chroma_cqt_arr = cqt_obj.chroma(cqt_arr)

# Display
import matplotlib.pyplot as plt
from audioflux.display import fill_spec
audio_len = audio_arr.shape[-1]

# Display CQT
fig, ax = plt.subplots()
img = fill_spec(np.abs(cqt_arr), axes=ax,
                x_coords=cqt_obj.x_coords(audio_len), x_axis='time',
                y_coords=cqt_obj.y_coords(), y_axis='log',
                title='CQT')
fig.colorbar(img, ax=ax)

# Display Chroma_cqt
fig, ax = plt.subplots()
img = fill_spec(chroma_cqt_arr, axes=ax,
                x_coords=cqt_obj.x_coords(audio_len),
                x_axis='time', y_axis='chroma',
                title='Chroma-CQT')
fig.colorbar(img, ax=ax)