Extract mfcc

Simple extraction of mfcc

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 mfcc
mfcc_arr, _ = af.mfcc(audio_arr, samplate=sr)

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

import numpy as np
import audioflux as af
from audioflux.type import SpectralFilterBankScaleType, SpectralDataType

# 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 BFT object and extract mel spectrogram
bft_obj = af.BFT(num=128, radix2_exp=12, samplate=sr,
                 scale_type=SpectralFilterBankScaleType.MEL,
                 data_type=SpectralDataType.POWER)
spec_arr = bft_obj.bft(audio_arr)
spec_arr = np.abs(spec_arr)

# Create XXCC object and extract mfcc
xxcc_obj = af.XXCC(bft_obj.num)
xxcc_obj.set_time_length(time_length=spec_arr.shape[-1])
mfcc_arr = xxcc_obj.xxcc(spec_arr)

# Display MFCC
import matplotlib.pyplot as plt
from audioflux.display import fill_spec
audio_len = audio_arr.shape[-1]
fig, ax = plt.subplots()
img = fill_spec(mfcc_arr, axes=ax,
          x_coords=bft_obj.x_coords(audio_len), x_axis='time',
          title='MFCC')
fig.colorbar(img, ax=ax)

Similar to mfcc, you can obtain various cepstral coefficients by adjusting the parameters of BFT and XXCC, such as bfcc/gtcc, etc.