BSS (Blind Source Separation)
This repository includes two different types of FDICA, IVA, AuxIVA, ILRMA as well as simultaneous video-audio-recording, speaker recognition and facial recognition.
工学博覧会2019 : 音声処理班音源分離チーム
Demo
You can watch the demonstration from HERE
Blind Source Separation
FDICA(version1, version2)
FDICA is frequency domain independent component analysis. The difference of those two versions are the way of permutation.
Requirements (library dependency)
You need Python 3.4 or later to run FDICA.
- munkres
- tqdm
- numpy
- scipy
Quick start
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])Usage
You can choose three different fai function.
Reference
- 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
IVA is independent vector analysis.
Requirements (library dependency)
You need Python 3.6 or later to run IVA.
- tqdm
- numpy
- scipy
Quick start
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])Reference
- Blind Source Separation Exploiting Higher-Order Frequency Dependencies
AuxIVA
AuxIVA is independent vector analysis.
Requirements (library dependency)
You need Python 3.6 or later to run AuxIVA.
- tqdm
- numpy
- scipy
Quick start
First, install libraries and change the current directory to src.
cd src
Second, for instance,
import numpy as np
import cis
from AuxIVA import AuxIVA
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 = AuxIVA(x, sample_freq=fs, beta=0.3).auxiva()
cis.wavwrite('./samples/music1_r.wav', fs, y[0])
cis.wavwrite('./samples/music2_r.wav', fs, y[1])
cis.wavwrite('./samples/music3_r.wav', fs, y[2])Reference
- 補助関数法による最適化アルゴリズ厶 とその音響信号処理への応用
ILRMA
ILRMA is Independent Low-Rank Matrix Analysis.
Requirements (library dependency)
You need Python 3.6 or later to run ILRMA.
- tqdm
- numpy
- scipy
Quick start
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])Reference
- Blind Source Separation Based on Independent Low-Rank Matrix Analysis
Speaker Recognition
MFCC is used to recognize speaker
Requirements (library dependency)
- numpy
- scipy
- librosa
- sklearn
- pickle
Facial Recognition
Requirments (Library dependency)
- opencv-python
- opencv-contrib-python
- moviepy
Contributing
Contributing to this project is always welcome.
License
This project is licensed under the MIT License.
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