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ProjectBlog: Separating mixed signals with Independent Component Analysis

### Blog: Separating mixed signals with Independent Component Analysis

The world around is a dynamic mixture of signals from various sources. Just like the colors in the above picture blend into one another, giving rise to new shades and tones, everything we perceive is a fusion of simpler components. Most of the time we are not even aware that the world around us is such a chaotic intermix of independent processes. Only in situations where different stimuli, that do not mix well, compete for our attention we realize this mess. A typical example is the scenario at a cocktail party where one is listening to the voice of another person while filtering out the voices of all the other guests. Depending on the loudness in the room this can either be a simple or a hard task but somehow our brains are capable of separating the signal from the noise. While it is not understood how our brains do this separation there are several computational techniques out there that aim at splitting a signal into its fundamental components. One of these methods is termed Independent Component Analysis (ICA) and here we will have a closer look on how this algorithm works and how to write it down in Python code. If you are more interested in the code than in the explanation you can also directly check out the Jupyter Notebook for this post on Github.

### What is Independent Component Analysis?

Lets stay with the example of the cocktail party for now. Imaging there are two people talking, you can hear both of them but one is closer to you than the other. The sound waves of both sources will mix and reach your ears as a combined signal. Your brain will un-mix both sources and you will perceive the voices of both guests separately with the one standing closer to you as the louder one. Now lets describe this in a more abstract and simplified way. Each source is a sine wave with a constant frequency. Both sources mix depending on where you stand. This means the source closer to you will be more dominant in the mixed signal than the one more far away. We can write this down as follows in vector-matrix notation:

Where x is the observed signal, s are the source signals and A is the mixing matrix. In other words our model assumes that the signals x are generated through a linear combination of the source signals. In Python code our example will look like this:

`>> import numpy as np`
`>>> # Number of samples >>> ns = np.linspace(0, 200, 1000)`
`>>> # Sources with (1) sine wave, (2) saw tooth and (3) random noise>>> S = np.array([np.sin(ns * 1),  signal.sawtooth(ns * 1.9), np.random.random(len(ns))]).T`
`>>> # Quadratic mixing matrix>>> A = np.array([[0.5, 1, 0.2], [1, 0.5, 0.4], [0.5, 0.8, 1]])`
`>>> # Mixed signal matrix>>> X = S.dot(A).T`

As can be seen from the plots in Figure 1 below the code generates one sine wave signal, one saw tooth signal and some random noise. These three signals are our independent sources. In the plot below we can also see the three linear combinations of the source signals. Further we see that the first mixed signal is dominated by the saw tooth component, the second mixed signal is influence more by the sine wave component and the last mixed signal is dominated by the noise component. Figure 1: Source signals (upper plots) and linear combinations of the source signals (lower plots).

Now, according to our model we can retrieve the source signals again from the mixed signals by multiplying x with the inverse of A:

This means in order to find the source signals we need to calculate W. So the task for the rest of this post will be to find W and retrieve the two independent source signals from the two mixed signals.

### Preconditions for the ICA to work

Now, before we continue we need to think a little more about what properties our source signals need to have so that the ICA successfully estimates W. The first precondition for the algorithm to work is that the mixed signals are a linear combination of any number of source signals. The second precondition is that the source signals are independent. So what does independence mean? Two signals are independent if the information in signal s1 does not give any information about signal s2. This implies that they are not correlated, which means that their covariance is 0. However, one has to be careful here as uncorrelatedness does not automatically mean independence. The third precondition is that the independent components are non-Gaussian. Why is that? The joint density distribution of two independent non-Gaussian signals will be uniform on a square; see upper left plot in Figure 2 below. Mixing these two signals with an orthogonal matrix will result in two signals that are now not independent anymore and have a uniform distribution on a parallelogram; see upper right plot in Figure 2. Which means that if we are at the minimum or maximum value of one of our mixed signals we know the value of the other signal. Therefore they are not independent anymore. Doing the same with two Gaussian signals will result in something else (see lower panel of Figure 2). The joint distribution of the source signals is completely symmetric and so is the joint distribution of the mixed signals. Therefore it does not contain any information about the mixing matrix, the inverse of which we want to calculate. It follows that in this case the ICA algorithm will fail.

So in summary for the ICA algorithm to work the following preconditions need to be met: Our sources are a (1) lineare mixture of (2) independent, (3) non-Gaussian signals.

So lets quickly check if our test signals from above meet these preconditions. In the left plot below we see the sine wave signal plottet against the saw tooth signal while the color of each dot represents the noise component. The signals are distributed on a square as expected for non-Gaussian random variables. Likewise the mixed signals form a parallelogram in the right plot of Figure 3 which shows that the mixed signals are not independent anymore.

### Pre-processing steps

Now taking the mixed signals and feeding them directly into the ICA is not a good idea. To get an optimal estimate of the independent components it is advisable to do some pre-processing of the data. In the following the two most important pre-processing techniques are explained in more detail.

#### Centering

The first pre-processing step we will discuss here is centering. This is a simple subtraction of the mean from our input X. As a result the centered mixed signals will have zero mean which implies that also our source signals s are of zero mean. This simplifies the ICA calculation and the mean can later be added back. The centering function in Python looks as follows.

`>>> def center(x):>>> return x - np.mean(x, axis=1, keepdims=True)`

#### Whitening

The second pre-processing step that we need is whitening of our signals X. The goal here is to linearly transform X so that potential correlations between the signals are removed and their variances equal unity. As a result the covariance matrix of the whitened signals will be equal to the identity matrix:

Where I is the identity matrix. Since we also need to calculate the covariance during the whitening procedure we will write a small Python function for it.

`>>> def covariance(x):>>> mean = np.mean(x, axis=1, keepdims=True)>>> n = np.shape(x) - 1>>> m = x - mean>>> return (m.dot(m.T))/n`

The code for the whitening step is shown below. It is based on the Singular Value Decomposition (SVD) of the covariance matrix of X. If you are interested in the details of this procedure I recommend this article.

`>>> def whiten(x):>>> # Calculate the covariance matrix>>> coVarM = covariance(X) `
`>>> # Singular value decoposition>>> U, S, V = np.linalg.svd(coVarM)>>> # Calculate diagonal matrix of eigenvalues>>> d = np.diag(1.0 / np.sqrt(S)) >>> # Calculate whitening matrix>>> whiteM = np.dot(U, np.dot(d, U.T))>>> # Project onto whitening matrix>>> Xw = np.dot(whiteM, X) >>> return Xw, whiteM`

### Implementation of the FastICA Algorithm

OK, now that we have our pre-processing functions in place we can finally start implementing the ICA algorithm. There are several ways of implementing the ICA based on the contrast function that measures independence. Here we will use an approximation of negentropy in an ICA version called FastICA.

So how does it work? As discussed above one precondition for ICA to work is that our source signals are non-Gaussian. An interesting thing about two independent, non-Gaussian signals is that their sum is more Gaussian than any of the source signals. Therefore we need to optimize W in a way that the resulting signals of Wx are as non-Gaussian as possible. In order to do so we need a measure of gaussianity. The simplest measure would be kurtosis, which is the fourth moment of the data and measures the “tailedness” of a distribution. A normal distribution has a value of 3, a uniform distribution like the one we used in Figure 2 has a kurtosis < 3. The implementation in Python is straight forward as can be seen from the code below which also calculates the other moments of the data. The first moment is the mean, the second is the variance, the third is the skewness and the fourth is the kurtosis. Here 3 is subtracted from the fourth moment so that a normal distribution has a kurtosis of 0.

`>>> def kurtosis(x):>>> n = np.shape(x)>>> mean = np.sum((x**1)/n) # Calculate the mean>>> var = np.sum((x-mean)**2)/n # Calculate the variance>>> skew = np.sum((x-mean)**3)/n # Calculate the skewness>>> kurt = np.sum((x-mean)**4)/n # Calculate the kurtosis>>> kurt = kurt/(var**2)-3`
`>>> return kurt, skew, var, mean`

For our implementation of ICA however we will not use kurtosis as a contrast function but we can use it later to check our results. Instead we will use the following contrast function g(u) and its first derivative g’(u):

The FastICA algorithm uses the two above functions in the following way in a fixed-point iteration scheme:

So according to the above what we have to do is to take a random guess for the weights of each component. The dot product of the random weights and the mixed signals is passed into the two functions g and g’. We then subtract the result of g’ from g and calculate the mean. The result is our new weights vector. Next we could directly divide the new weights vector by its norm and repeat the above until the weights do not change anymore. There would be nothing wrong with that. However the problem we are facing here is that in the iteration for the second component we might identify the same component as in the first iteration. To solve this problem we have to decorrelate the new weights from the previously identified weights. This is what is happening in the step between updating the weights and dividing by their norm. In Python the implementation then looks as follows:

`>>> def fastIca(signals, alpha = 1, thresh=1e-8, iterations=5000):>>> m, n = signals.shape`
`>>> # Initialize random weights>>> W = np.random.rand(m, m)`
`>>> for c in range(m):>>> w = W[c, :].copy().reshape(m, 1)>>> w = w/ np.sqrt((w ** 2).sum())`
`>>> i = 0>>> lim = 100>>> while ((lim > thresh) & (i < iterations)):`
`>>> # Dot product of weight and signal>>> ws = np.dot(w.T, signals)`
`>>> # Pass w*s into contrast function g>>> wg = np.tanh(ws * alpha).T`
`>>> # Pass w*s into g'>>> wg_ = (1 - np.square(np.tanh(ws))) * alpha`
`>>> # Update weights wNew = (signals * wg.T).mean(axis=1) - >>> wg_.mean() * w.squeeze()`
`>>> # Decorrelate weights >>> wNew = wNew -  np.dot(np.dot(wNew, W[:c].T), W[:c])>>> wNew = wNew / np.sqrt((wNew ** 2).sum())`
`>>> # Calculate limit condition>>> lim = np.abs(np.abs((wNew * w).sum()) - 1)>>> # Update weights>>> w = wNew>>> # Update counter>>> i += 1`
`>>> W[c, :] = w.T>>> return W`

So now that we have all the code written up, lets run the whole thing!

`>>> # Center signals>>> Xc, meanX = center(X)`
`>>> # Whiten mixed signals>>> Xw, whiteM = whiten(Xc)`
`>>> # Run the ICA to estimate W>>> W = fastIca(Xw, alpha=1)`
`>>> #Un-mix signals using W>>> unMixed = Xw.T.dot(W.T)`
`>>> # Subtract mean from the unmixed signals>>> unMixed = (unMixed.T - meanX).T`

The results of the ICA are shown in Figure 4 below where the upper panel represents the original source signals and the lower panel the independent components retrieved by our ICA implementation. And the result looks very good. We got all three sources back! Figure 4: Results of the ICA analysis. Above true sources; below recovered signals.

So finally lets check one last thing: The kurtosis of the signals. As we can see in Figure 5 all of our mixed signals have a kurtosis of ≤ 1 whereas all recovered independent components have a kurtosis of 1.5 which means they are less Gaussian than their sources. This has to be the case since the ICA tries to maximize non-Gaussianity. Also it nicely illustrates the fact mentioned above that the mixture of non-Gaussian signals will be more Gaussian than the sources. Figure 5: Kernel Density Estimates of the three mixed and source signals.

So to summarize: We saw how ICA works and how to implement it from scratch in Python. Of course there are many Python implementations available that can be directly used. However it is always advisable to understand the underlying principle of the method to know when and how to use it. If you are interested in diving deeper into ICA and learn about the details I recommend this paper by Aapo Hyvärinen and Erkki Oja, 2000.

Otherwise you can check out the complete code here, follow me on Twitter or connect via LinkedIn.

The code for this project can be found on Github.