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<div class="section">
<div class="titlepage"><div><div><h2 class="title" style="clear: both">
<a name="math_toolkit.signal_statistics"></a><a class="link" href="signal_statistics.html" title="Signal Statistics">Signal Statistics</a>
</h2></div></div></div>
<h4>
<a name="math_toolkit.signal_statistics.h0"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.synopsis"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.synopsis">Synopsis</a>
</h4>
<pre class="programlisting"><span class="preprocessor">#include</span> <span class="special"><</span><span class="identifier">boost</span><span class="special">/</span><span class="identifier">math</span><span class="special">/</span><span class="identifier">statistics</span><span class="special">/</span><span class="identifier">signal_statistics</span><span class="special">.</span><span class="identifier">hpp</span><span class="special">></span>
<span class="keyword">namespace</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span> <span class="special">{</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">Container</span> <span class="special">&</span> <span class="identifier">c</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">sample_absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">Container</span> <span class="special">&</span> <span class="identifier">c</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">sample_absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">hoyer_sparsity</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">c</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">hoyer_sparsity</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">oracle_snr</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">signal</span><span class="special">,</span> <span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">noisy_signal</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">oracle_snr_db</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">signal</span><span class="special">,</span> <span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">noisy_signal</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">m2m4_snr_estimator</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">,</span> <span class="keyword">decltype</span><span class="special">(*</span><span class="identifier">first</span><span class="special">)</span> <span class="identifier">estimated_signal_kurtosis</span><span class="special">=</span><span class="number">1</span><span class="special">,</span> <span class="keyword">decltype</span><span class="special">(*</span><span class="identifier">first</span><span class="special">)</span> <span class="identifier">estimated_noise_kurtosis</span><span class="special">=</span><span class="number">3</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">m2m4_snr_estimator</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">noisy_signal</span><span class="special">,</span> <span class="keyword">typename</span> <span class="identifier">Container</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">estimated_signal_kurtosis</span><span class="special">=</span><span class="number">1</span><span class="special">,</span> <span class="keyword">typename</span> <span class="identifier">Container</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">estimate_noise_kurtosis</span><span class="special">=</span><span class="number">3</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">ForwardIterator</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">m2m4_snr_estimator_db</span><span class="special">(</span><span class="identifier">ForwardIterator</span> <span class="identifier">first</span><span class="special">,</span> <span class="identifier">ForwardIterator</span> <span class="identifier">last</span><span class="special">,</span> <span class="keyword">decltype</span><span class="special">(*</span><span class="identifier">first</span><span class="special">)</span> <span class="identifier">estimated_signal_kurtosis</span><span class="special">=</span><span class="number">1</span><span class="special">,</span> <span class="keyword">decltype</span><span class="special">(*</span><span class="identifier">first</span><span class="special">)</span> <span class="identifier">estimated_noise_kurtosis</span><span class="special">=</span><span class="number">3</span><span class="special">);</span>
<span class="keyword">template</span><span class="special"><</span><span class="keyword">class</span> <span class="identifier">Container</span><span class="special">></span>
<span class="keyword">auto</span> <span class="identifier">m2m4_snr_estimator_db</span><span class="special">(</span><span class="identifier">Container</span> <span class="keyword">const</span> <span class="special">&</span> <span class="identifier">noisy_signal</span><span class="special">,</span><span class="keyword">typename</span> <span class="identifier">Container</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">estimated_signal_kurtosis</span><span class="special">=</span><span class="number">1</span><span class="special">,</span> <span class="keyword">typename</span> <span class="identifier">Container</span><span class="special">::</span><span class="identifier">value_type</span> <span class="identifier">estimate_noise_kurtosis</span><span class="special">=</span><span class="number">3</span><span class="special">);</span>
<span class="special">}</span>
</pre>
<h4>
<a name="math_toolkit.signal_statistics.h1"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.description"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.description">Description</a>
</h4>
<p>
The file <code class="computeroutput"><span class="identifier">boost</span><span class="special">/</span><span class="identifier">math</span><span class="special">/</span><span class="identifier">statistics</span><span class="special">/</span><span class="identifier">signal_statistics</span><span class="special">.</span><span class="identifier">hpp</span></code> is a
set of facilities for computing quantities commonly used in signal analysis.
</p>
<p>
Our examples use <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="keyword">double</span><span class="special">></span></code> to
hold the data, but this not required. In general, you can store your data in
an Eigen array, and Armadillo vector, <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">array</span></code>,
and for many of the routines, a <code class="computeroutput"><span class="identifier">std</span><span class="special">::</span><span class="identifier">forward_list</span></code>.
These routines are usable in float, double, long double, and Boost.Multiprecision
precision, as well as their complex extensions whenever the computation is
well-defined.
</p>
<h4>
<a name="math_toolkit.signal_statistics.h2"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.absolute_gini_coefficient"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.absolute_gini_coefficient">Absolute
Gini Coefficient</a>
</h4>
<p>
The Gini coefficient, first used to measure wealth inequality, is also one
of the best measures of the sparsity of an expansion in a basis. A sparse expansion
has most of its norm concentrated in just a few coefficients, making the connection
with wealth inequality obvious. See <a href="https://arxiv.org/pdf/0811.4706.pdf" target="_top">Hurley
and Rickard</a> for details. However, for measuring sparsity, the phase
of the numbers is irrelevant, so we provide the <code class="computeroutput"><span class="identifier">absolute_gini_coefficient</span></code>:
</p>
<pre class="programlisting"><span class="keyword">using</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">sample_absolute_gini_coefficient</span><span class="special">;</span>
<span class="keyword">using</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">absolute_gini_coefficient</span><span class="special">;</span>
<span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="identifier">std</span><span class="special">::</span><span class="identifier">complex</span><span class="special"><</span><span class="keyword">double</span><span class="special">>></span> <span class="identifier">v</span><span class="special">{{</span><span class="number">0</span><span class="special">,</span><span class="number">1</span><span class="special">},</span> <span class="special">{</span><span class="number">0</span><span class="special">,</span><span class="number">0</span><span class="special">},</span> <span class="special">{</span><span class="number">0</span><span class="special">,</span><span class="number">0</span><span class="special">},</span> <span class="special">{</span><span class="number">0</span><span class="special">,</span><span class="number">0</span><span class="special">}};</span>
<span class="keyword">double</span> <span class="identifier">abs_gini</span> <span class="special">=</span> <span class="identifier">sample_absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="comment">// now abs_gini = 1; maximally unequal</span>
<span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="identifier">std</span><span class="special">::</span><span class="identifier">complex</span><span class="special"><</span><span class="keyword">double</span><span class="special">>></span> <span class="identifier">w</span><span class="special">{{</span><span class="number">0</span><span class="special">,</span><span class="number">1</span><span class="special">},</span> <span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">0</span><span class="special">},</span> <span class="special">{</span><span class="number">0</span><span class="special">,-</span><span class="number">1</span><span class="special">},</span> <span class="special">{-</span><span class="number">1</span><span class="special">,</span><span class="number">0</span><span class="special">}};</span>
<span class="identifier">abs_gini</span> <span class="special">=</span> <span class="identifier">absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">w</span><span class="special">);</span>
<span class="comment">// now abs_gini = 0; every element of the vector has equal magnitude</span>
<span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="keyword">double</span><span class="special">></span> <span class="identifier">u</span><span class="special">{-</span><span class="number">1</span><span class="special">,</span> <span class="number">1</span><span class="special">,</span> <span class="special">-</span><span class="number">1</span><span class="special">};</span>
<span class="identifier">abs_gini</span> <span class="special">=</span> <span class="identifier">absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">u</span><span class="special">);</span>
<span class="comment">// now abs_gini = 0</span>
<span class="comment">// Alternative call useful for computing over subset of the input:</span>
<span class="identifier">abs_gini</span> <span class="special">=</span> <span class="identifier">absolute_gini_coefficient</span><span class="special">(</span><span class="identifier">u</span><span class="special">.</span><span class="identifier">begin</span><span class="special">(),</span> <span class="identifier">u</span><span class="special">.</span><span class="identifier">begin</span><span class="special">()</span> <span class="special">+</span> <span class="number">1</span><span class="special">);</span>
</pre>
<p>
The sample Gini coefficient returns unity for a vector which has only one nonzero
coefficient. The population Gini coefficient of a vector with one non-zero
element is dependent on the length of the input.
</p>
<p>
The sample Gini coefficient lacks one desirable property of the population
Gini coefficient, namely that "cloning" a vector has the same Gini
coefficient; though cloning holds to very high accuracy with the sample Gini
coefficient and can easily be recovered by a rescaling.
</p>
<p>
If sorting the input data is too much expense for a sparsity measure (is it
going to be perfect anyway?), consider calculating the Hoyer sparsity instead.
</p>
<h4>
<a name="math_toolkit.signal_statistics.h3"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.hoyer_sparsity"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.hoyer_sparsity">Hoyer
Sparsity</a>
</h4>
<p>
The Hoyer sparsity measures a normalized ratio of the ℓ<sup>1</sup> and ℓ<sup>2</sup> norms.
As the name suggests, it is used to measure the sparsity of an expansion in
some basis.
</p>
<p>
The Hoyer sparsity computes (√<span class="emphasis"><em>N</em></span> - ℓ<sup>1</sup>(v)/ℓ<sup>2</sup>(v))/(√N
-1). For details, see <a href="http://www.jmlr.org/papers/volume5/hoyer04a/hoyer04a.pdf" target="_top">Hoyer</a>
as well as <a href="https://arxiv.org/pdf/0811.4706.pdf" target="_top">Hurley and Rickard</a>.
</p>
<p>
A few special cases will serve to clarify the intended use: If <span class="emphasis"><em>v</em></span>
has only one nonzero coefficient, the Hoyer sparsity attains its maxima of
1. If the coefficients of <span class="emphasis"><em>v</em></span> all have the same magnitude,
then the Hoyer sparsity attains its minima of zero. If the elements of <span class="emphasis"><em>v</em></span>
are uniformly distributed on an interval [0, <span class="emphasis"><em>b</em></span>], then
the Hoyer sparsity is approximately 0.133.
</p>
<p>
Usage:
</p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="identifier">Real</span><span class="special">></span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,</span><span class="number">0</span><span class="special">,</span><span class="number">0</span><span class="special">};</span>
<span class="identifier">Real</span> <span class="identifier">hs</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">hoyer_sparsity</span><span class="special">(</span><span class="identifier">v</span><span class="special">);</span>
<span class="comment">// hs = 1</span>
<span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="identifier">Real</span><span class="special">></span> <span class="identifier">v</span><span class="special">{</span><span class="number">1</span><span class="special">,-</span><span class="number">1</span><span class="special">,</span><span class="number">1</span><span class="special">};</span>
<span class="identifier">Real</span> <span class="identifier">hs</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">hoyer_sparsity</span><span class="special">(</span><span class="identifier">v</span><span class="special">.</span><span class="identifier">begin</span><span class="special">(),</span> <span class="identifier">v</span><span class="special">.</span><span class="identifier">end</span><span class="special">());</span>
<span class="comment">// hs = 0</span>
</pre>
<p>
The container must be forward iterable and the contents are not modified. Accepts
real, complex, and integer inputs. If the input is an integral type, the output
is a double precision float.
</p>
<h4>
<a name="math_toolkit.signal_statistics.h4"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.oracle_signal_to_noise_ratio"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.oracle_signal_to_noise_ratio">Oracle
Signal-to-noise ratio</a>
</h4>
<p>
The function <code class="computeroutput"><span class="identifier">oracle_snr</span></code> computes
the ratio ‖ <span class="emphasis"><em>s</em></span> ‖<sub>2</sub><sup>2</sup> / ‖ <span class="emphasis"><em>s</em></span>
- <span class="emphasis"><em>x</em></span> ‖<sub>2</sub><sup>2</sup>, where <span class="emphasis"><em>s</em></span> is signal
and <span class="emphasis"><em>x</em></span> is a noisy signal. The function <code class="computeroutput"><span class="identifier">oracle_snr_db</span></code>
computes 10<code class="computeroutput"><span class="identifier">log</span></code><sub>10</sub>(‖
<span class="emphasis"><em>s</em></span> ‖<sup>2</sup> / ‖ <span class="emphasis"><em>s</em></span> - <span class="emphasis"><em>x</em></span>
‖<sup>2</sup>). The functions are so named because in general, one does not know
how to decompose a real signal <span class="emphasis"><em>x</em></span> into <span class="emphasis"><em>s</em></span>
+ <span class="emphasis"><em>w</em></span> and as such <span class="emphasis"><em>s</em></span> is regarded as
oracle information. Hence this function is mainly useful for unit testing other
SNR estimators.
</p>
<p>
Usage:
</p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="keyword">double</span><span class="special">></span> <span class="identifier">signal</span><span class="special">(</span><span class="number">500</span><span class="special">,</span> <span class="number">3.2</span><span class="special">);</span>
<span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="keyword">double</span><span class="special">></span> <span class="identifier">noisy_signal</span><span class="special">(</span><span class="number">500</span><span class="special">);</span>
<span class="comment">// fill 'noisy_signal' signal + noise</span>
<span class="keyword">double</span> <span class="identifier">snr_db</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">oracle_snr_db</span><span class="special">(</span><span class="identifier">signal</span><span class="special">,</span> <span class="identifier">noisy_signal</span><span class="special">);</span>
<span class="keyword">double</span> <span class="identifier">snr</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">oracle_snr</span><span class="special">(</span><span class="identifier">signal</span><span class="special">,</span> <span class="identifier">noisy_signal</span><span class="special">);</span>
</pre>
<p>
The input can be real, complex, or integral. Integral inputs produce double
precision floating point outputs. The input data is not modified and must satisfy
the requirements of a <code class="computeroutput"><span class="identifier">RandomAccessContainer</span></code>.
</p>
<h4>
<a name="math_toolkit.signal_statistics.h5"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.m_sub_2_m_sub_4_snr_estimation"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.m_sub_2_m_sub_4_snr_estimation"><span class="emphasis"><em>M</em></span><sub>2</sub><span class="emphasis"><em>M</em></span><sub>4</sub> SNR
Estimation</a>
</h4>
<p>
Estimates the SNR of a noisy signal via the <span class="emphasis"><em>M</em></span><sub>2</sub><span class="emphasis"><em>M</em></span><sub>4</sub> method.
See <a href="https://doi.org/10.1109/26.871393" target="_top">Pauluzzi and N.C. Beaulieu</a>
and <a href="https://doi.org/10.1109/ISIT.1994.394869" target="_top">Matzner and Englberger</a>
for details.
</p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="keyword">double</span><span class="special">></span> <span class="identifier">noisy_signal</span><span class="special">(</span><span class="number">512</span><span class="special">);</span>
<span class="comment">// fill noisy_signal with data contaminated by Gaussian white noise:</span>
<span class="keyword">double</span> <span class="identifier">est_snr_db</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">m2m4_snr_estimator_db</span><span class="special">(</span><span class="identifier">noisy_signal</span><span class="special">);</span>
</pre>
<p>
The <span class="emphasis"><em>M</em></span><sub>2</sub><span class="emphasis"><em>M</em></span><sub>4</sub> SNR estimator is an "in-service"
estimator, meaning that the estimate is made using the noisy, data-bearing
signal, and does not require a background estimate. This estimator has been
found to be work best between roughly -3 and 15db, tending to overestimate
the noise below -3db, and underestimate the noise above 15db. See <a href="https://www.mdpi.com/2078-2489/8/3/75/pdf" target="_top">Xue
et al</a> for details.
</p>
<p>
The <span class="emphasis"><em>M</em></span><sub>2</sub><span class="emphasis"><em>M</em></span><sub>4</sub> SNR estimator, by default,
assumes that the kurtosis of the signal is 1 and the kurtosis of the noise
is 3, the latter corresponding to Gaussian noise. These parameters, however,
can be overridden:
</p>
<pre class="programlisting"><span class="identifier">std</span><span class="special">::</span><span class="identifier">vector</span><span class="special"><</span><span class="keyword">double</span><span class="special">></span> <span class="identifier">noisy_signal</span><span class="special">(</span><span class="number">512</span><span class="special">);</span>
<span class="comment">// fill noisy_signal with the data:</span>
<span class="keyword">double</span> <span class="identifier">signal_kurtosis</span> <span class="special">=</span> <span class="number">1.5</span><span class="special">;</span>
<span class="comment">// Noise is assumed to follow Laplace distribution, which has kurtosis of 6:</span>
<span class="keyword">double</span> <span class="identifier">noise_kurtosis</span> <span class="special">=</span> <span class="number">6</span><span class="special">;</span>
<span class="keyword">double</span> <span class="identifier">est_snr</span> <span class="special">=</span> <span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">statistics</span><span class="special">::</span><span class="identifier">m2m4_snr_estimator_db</span><span class="special">(</span><span class="identifier">noisy_signal</span><span class="special">,</span> <span class="identifier">signal_kurtosis</span><span class="special">,</span> <span class="identifier">noise_kurtosis</span><span class="special">);</span>
</pre>
<p>
Now, technically the method is a "blind SNR estimator", meaning that
the no <span class="emphasis"><em>a-priori</em></span> information about the signal is required
to use the method. However, the performance of the method is <span class="emphasis"><em>vastly</em></span>
better if you can come up with a better estimate of the signal and noise kurtosis.
How can we do this? Suppose we know that the SNR is much greater than 1. Then
we can estimate the signal kurtosis simply by using the noisy signal kurtosis.
If the SNR is much less than one, this method breaks down as the noisy signal
kurtosis will tend to the noise kurtosis-though in this limit we have an excellent
estimator of the noise kurtosis! In addition, if you have a model of what your
signal should look like, you can precompute the signal kurtosis. For example,
sinusoids have a kurtosis of 1.5. See <a href="http://www.jcomputers.us/vol8/jcp0808-21.pdf" target="_top">here</a>
for a study which uses estimates of this sort to improve the performance of
the <span class="emphasis"><em>M</em></span><sub>2</sub><span class="emphasis"><em>M</em></span><sub>4</sub> estimator.
</p>
<p>
<span class="emphasis"><em>Nota bene</em></span>: The traditional definition of SNR is <span class="emphasis"><em>not</em></span>
mean invariant. By this we mean that if a constant is added to every sample
of a signal, the SNR is changed. For example, adding DC bias to a signal changes
its SNR. For most use cases, this is really not what you intend; for example
a signal consisting of zeros plus Gaussian noise has an SNR of zero, whereas
a signal with a constant DC bias and random Gaussian noise might have a very
large SNR.
</p>
<p>
The <span class="emphasis"><em>M</em></span><sub>2</sub><span class="emphasis"><em>M</em></span><sub>4</sub> SNR estimator is computed
from mean-invariant quantities, and hence it should really be compared to the
mean-invariant SNR.
</p>
<p>
<span class="emphasis"><em>Nota bene</em></span>: This computation requires the solution of a
system of quadratic equations involving the noise kurtosis, the signal kurtosis,
and the second and fourth moments of the data. There is no guarantee that a
solution of this system exists for all value of these parameters, in fact nonexistence
can easily be demonstrated for certain data. If there is no solution to the
system, then failure is communicated by returning NaNs. This happens distressingly
often; if a user is aware of any blind SNR estimators which do not suffer from
this drawback, please open a github ticket and let us know.
</p>
<p>
The author has not managed to fully characterize the conditions under which
a real solution with <span class="emphasis"><em>S > 0</em></span> and <span class="emphasis"><em>N >0</em></span>
exists. However, a very intuitive example demonstrates why nonexistence can
occur. Suppose the signal and noise kurtosis are equal. Then the method has
no way to distinguish between the signal and the noise, and the solution is
non-unique.
</p>
<h4>
<a name="math_toolkit.signal_statistics.h6"></a>
<span class="phrase"><a name="math_toolkit.signal_statistics.references"></a></span><a class="link" href="signal_statistics.html#math_toolkit.signal_statistics.references">References</a>
</h4>
<div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; ">
<li class="listitem">
Mallat, Stephane. <span class="emphasis"><em>A wavelet tour of signal processing: the sparse
way.</em></span> Academic press, 2008.
</li>
<li class="listitem">
Hurley, Niall, and Scott Rickard. <span class="emphasis"><em>Comparing measures of sparsity.</em></span>
IEEE Transactions on Information Theory 55.10 (2009): 4723-4741.
</li>
<li class="listitem">
Jensen, Arne, and Anders la Cour-Harbo. <span class="emphasis"><em>Ripples in mathematics:
the discrete wavelet transform.</em></span> Springer Science & Business
Media, 2001.
</li>
<li class="listitem">
D. R. Pauluzzi and N. C. Beaulieu, <span class="emphasis"><em>A comparison of SNR estimation
techniques for the AWGN channel,</em></span> IEEE Trans. Communications,
Vol. 48, No. 10, pp. 1681-1691, 2000.
</li>
<li class="listitem">
Hoyer, Patrik O. <span class="emphasis"><em>Non-negative matrix factorization with sparseness
constraints.</em></span>, Journal of machine learning research 5.Nov (2004):
1457-1469.
</li>
</ul></div>
</div>
<div class="copyright-footer">Copyright © 2006-2021 Nikhar Agrawal, Anton Bikineev, Matthew Borland,
Paul A. Bristow, Marco Guazzone, Christopher Kormanyos, Hubert Holin, Bruno
Lalande, John Maddock, Evan Miller, Jeremy Murphy, Matthew Pulver, Johan Råde,
Gautam Sewani, Benjamin Sobotta, Nicholas Thompson, Thijs van den Berg, Daryle
Walker and Xiaogang Zhang<p>
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
</p>
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