I have a range of songs where I have took an 'unfolded' chromagram computed from a series of short-term windows. Specifically, for one song, I have one vector which contains the amplitudes of the chroma's, and a second vector which contains the corresponding MIDI notes. For now, I can plot a histogram of the amplitudes w.r.t the MIDI notes that gives me a visual idea of the range of notes used.

Here are two songs graphed:


As we can see, the 'classical' example is in my opinion more sparse than its neighbouring graph. I've been trying to figure out a way as to how to measure for something like this, since I need to fold such a feature vector into 1 useful statistic.

  • $\begingroup$ Probably you can use sparsity as a useful statistic. The sparsity of a vector is the number of non-zero components of the vector. It is denoted mathematically using $L_0$-norm which is popular in sparse representation and in compressed sensing. For example, if $x=[1 0 0 -1 2.3 0 0 0 0 1 0 0 0 0]$, which of length $N=14$, its sparsity is, that is, $L_0$-norm is $\|x \|_0=4$. It is a 4-sparse vector. $\endgroup$ – Oliver Jun 13 '15 at 1:08
  • $\begingroup$ Sorry there some notation error in the above comment. The $$x=\left[1~0~ ~0~-1~2.3~0~0~0~0~1~0~0~0~0~\right]$$ $\endgroup$ – Oliver Jun 13 '15 at 1:28
  • $\begingroup$ Thanks for the advice, I'll look into it this and let you know how I get on. $\endgroup$ – user1574598 Jun 16 '15 at 8:41

I am considering a similar problem as well and I found Gini index as one of the solution: Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples. Please have a look at the definition in page 3. It is normalized in the range from 0 to 1 as the least sparse or the most sparse the vector is, respectively.

The code below is an implementation in Python, though I am not sure about returning 0 if the input is a zero vector:

def gini(v):
    v = np.array(v)
    sort_v = sort(abs(v))
    norm_v = sum(sort_v)
    if norm_v==0:
        sig = 0 
        for k in arange(len(sort_v))+1:

In Comparing Measures of Sparsity, 2009, IEEE Trans. Information Theory, an axiomatic characterization of sparsity for random sources, and a comparison of different measures is provided. Two measures emerged (in my reading): the Gini index or coefficient, as proposed above, and the $\ell_1/\ell_2$ norm ratio, illustrated below.

enter image description here

It stems from a standard norm inequality between $\ell_1$ and $\ell_2$ ($\ell_2 \le \ell_1 \le \sqrt{n}\ell_2$, with $n$ the length of the signal, through Cauchy-Schwarz or Hölder inequality), that saturates on both ends for:

  • the nonzero-constant signal,
  • the all-zero-but-one signal,

and being scale-invariant, zero-homogeneous (as the Gini index or the $\ell_0$ counting index).

While the Gini index seems the more natural, the second one can be used more easily in subsequent processing, for instance restoration, as it provides a computable non-convex penalty. For a recent paper with an historical account for the norm ratio: Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed $\ell _1/\ell _2$ Regularization, 2015, IEEE Signal Processing Letters.

A Matlab code is given below:

function mos_l1_l2 = MoS_Hoyer_ratio_l1_l2(data)

if isequal(data,0)
  mos_l1_l2 = 0;
  mos_l1_l2 = sum(abs(data))./sqrt(sum((data).^2));

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