Uml Ica

#Blind Source Separation (Cocktail Party Problem) using ICA
#Objective: To apply ICA for separating mixed audio signals into their original independent sources

#Steps to Implement ICA Generate or Load Mixed Signals:

#We will use artificially mised signals that represent two speakers talking at the same time.
#Preprocess Data: Normalize and standardize the mixed signals.
#Apply ICA Algorithm: Use the FastICA algorithm from sklearn.decomposition to separate independent signals.
#Evaluate Results: Plot and listen/visualize to the separated signals to verify the separation.

import numpy as np
import matplotlib.pyplot as plt 
from sklearn.decomposition import FastICA 
from scipy.io import wavfile 
import soundfile as sf 

#Generate synthetic mixed signals 
def generate_signals(): 
    np.random.seed(8) 
    time = np.linspace(0, 10, 1000) 
    s1 = np.sin(2 * time) # Source 1: Sine wave 
    s2 = np.sign(np.sin(3 * time)) # Source 2: Square wave
    S  = np.c_[s1, s2]
    S /= S.std(axis=0)  # Standardize the signals

    # Mixing matrix
    A = np.array([[1,0.5], [0.5,1]])
    X = S @ A.T # Mixed signals
    return S, X

# Perform ICA for Blind Source Separation
def apply_ica(X):
    ica = FastICA(n_components = 2)
    S_ica = ica.fit_transform(X)  # Apply ICA
    return S_ica

# Plot results
def plot_results(S, X, S_ica):
    fig, axes = plt.subplots(3, 1, figsize=(10, 8))
    axes[0].plot(S)
    axes[0].set_title("Original Source Signals")
    axes[1].plot(X)
    axes[1].set_title("Mixed Signals")
    axes[2].plot(S_ica)
    axes[2].set_title("Separated Signals (ICA)")
    plt.tight_layout()
    plt.show()

# Running the ICA implementation
S, X = generate_signals()
S_ica = apply_ica(X)
plot_results(S, X, S_ica)