FDICA is frequency domain independent component analysis.
You need Python 3.4 or later to run FDICA.
- munkres
- tqdm
- numpy
- scipy
First, install libraries and change the current directory to src.
pip install numpy
pip install scipy
pip install tqdm
pip install munkres
cd src
Second, for instance,
import numpy as np
import scipy.io.wavfile as wf
from FDICA import ICA, FDICA
#prepare data
rate1, data1 = wf.read('./mix_1.wav')
rate2, data2 = wf.read('./mix_2.wav')
rate3, data3 = wf.read('./mix_3.wav')
if rate1 != rate2 or rate2 != rate3:
raise ValueError('Sampling_rate_Error')
data = [data1.astype(float), data2.astype(float), data3.astype(float)]
y = FDICA(data, sample_freq=rate1).fdica()
y = [(y_i * 32767 / max(np.absolute(y_i))).astype(np.int16) for y_i in np.asarray(y)]
wf.write('./music1.wav', rate1, y[0])
wf.write('./music2.wav', rate2, y[1])
wf.write('./music3.wav', rate3, y[2])You can choose three different fai function.
- Evaluation of blind signal separation method using directivity pattern under reverberant condition
- An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals.
IVA is independent vector analysis.
You need Python 3.6 or later to run IVA.
- tqdm
- numpy
- scipy
First, install libraries and change the current directory to src.
cd src
Second, for instance,
import numpy as np
import cis
from IVA import IVA
rate1, data1 = cis.wavread('./samples/mixdata/mix1.wav')
rate2, data2 = cis.wavread('./samples/mixdata/mix2.wav')
rate3, data3 = cis.wavread('./samples/mixdata/mix3.wav')
if rate1 != rate2 or rate2 != rate3:
raise ValueError('Sampling_rate_Error')
fs = rate1
x = np.array([data1, data2, data3], dtype=np.float32)
y = IVA(x, fs).iva()
cis.wavwrite('./samples/sepdata/IVA/music1_r.wav', fs, y[0])
cis.wavwrite('./samples/sepdata/IVA/music2_r.wav', fs, y[1])
cis.wavwrite('./samples/sepdata/IVA/music3_r.wav', fs, y[2])- Blind Source Separation Exploiting Higher-Order Frequency Dependencies
ILRMA is Independent Low-Rank Matrix Analysis.
You need Python 3.6 or later to run ILRMA.
- tqdm
- numpy
- scipy
First, install libraries and change the current directory to src.
cd src
Second, for instance,
import numpy as np
import cis
from ILRMA import ILRMA
rate1, data1 = cis.wavread('./samples/mixdata/mix1.wav')
rate2, data2 = cis.wavread('./samples/mixdata/mix2.wav')
rate3, data3 = cis.wavread('./samples/mixdata/mix3.wav')
if rate1 != rate2 or rate2 != rate3:
raise ValueError('Sampling_rate_Error')
fs = rate1
x = np.array([data1, data2, data3], dtype=np.float32)
y = ILRMA(x, fs, L=2).ilrma() # L is # of bases for each source
cis.wavwrite('./samples/sepdata/ilrma_1.wav', fs, y[0])
cis.wavwrite('./samples/sepdata/ilrma_2.wav', fs, y[1])
cis.wavwrite('./samples/sepdata/ilrma_3.wav', fs, y[2])- Blind Source Separation Based on Independent Low-Rank Matrix Analysis
MFCC is used to recognize speaker
- numpy
- scipy
- librosa
- sklearn
- pickle
- opencv-python
- opencv-contrib-python
- moviepy