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spectral features; mfcc; knn instrument classification
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">numpy</span><span class="o">,</span> <span class="nn">scipy</span><span class="o">,</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span><span class="o">,</span> <span class="nn">sklearn</span><span class="o">,</span> <span class="nn">librosa</span><span class="o">,</span> <span class="nn">urllib</span><span class="o">,</span> <span class="nn">IPython.display</span>
<span class="kn">import</span> <span class="nn">essentia</span><span class="o">,</span> <span class="nn">essentia.standard</span> <span class="kn">as</span> <span class="nn">ess</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
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<h1 id="Mel-Frequency-Cepstral-Coefficients-(MFCCs)">Mel Frequency Cepstral Coefficients (MFCCs)<a class="anchor-link" href="#Mel-Frequency-Cepstral-Coefficients-(MFCCs)">&#182;</a></h1>
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<p>The <a href="https://en.wikipedia.org/wiki/Mel-frequency_cepstrum">mel frequency cepstral coefficients</a> (MFCCs) of a signal are a small set of features (usually about 10-20) which concisely describe the overall shape of a spectral envelope. In MIR, it is often used to describe timbre.</p>
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<p>Download an audio file:</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">url</span> <span class="o">=</span> <span class="s">&#39;http://audio.musicinformationretrieval.com/simple_loop.wav&#39;</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s">&#39;simple_loop.wav&#39;</span><span class="p">)</span>
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<pre>(&apos;simple_loop.wav&apos;, &lt;httplib.HTTPMessage instance at 0x10ec5cd88&gt;)</pre>
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<div class=" highlight hl-ipython2"><pre><span class="n">x</span><span class="p">,</span> <span class="n">fs</span> <span class="o">=</span> <span class="n">librosa</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&#39;simple_loop.wav&#39;</span><span class="p">)</span>
<span class="n">librosa</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">waveplot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">fs</span><span class="p">)</span>
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<pre>&lt;matplotlib.collections.PolyCollection at 0x10ecb5fd0&gt;</pre>
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<h2 id="librosa.feature.mfcc"><code>librosa.feature.mfcc</code><a class="anchor-link" href="#librosa.feature.mfcc">&#182;</a></h2>
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<p><a href="http://bmcfee.github.io/librosa/generated/librosa.feature.mfcc.html#librosa.feature.mfcc"><code>librosa.feature.mfcc</code></a> computes MFCCs across an audio signal:</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">mfccs</span> <span class="o">=</span> <span class="n">librosa</span><span class="o">.</span><span class="n">feature</span><span class="o">.</span><span class="n">mfcc</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">fs</span><span class="p">)</span>
<span class="k">print</span> <span class="n">mfccs</span><span class="o">.</span><span class="n">shape</span>
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<pre>(20, 130)
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<p>In this case, <code>mfcc</code> computed 20 MFCCs over 130 frames.</p>

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<p>The very first MFCC, the 0th coefficient, does not convey information relevant to the overall shape of the spectrum. It only conveys a constant offset, i.e. adding a constant value to the entire spectrum. Therefore, many practitioners will discard the first MFCC when performing classification. For now, we will use the MFCCs as is.</p>

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<p>Display the MFCCs:</p>

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<div class=" highlight hl-ipython2"><pre><span class="n">librosa</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">specshow</span><span class="p">(</span><span class="n">mfccs</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">x_axis</span><span class="o">=</span><span class="s">&#39;time&#39;</span><span class="p">)</span>
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<div class="output_area"><div class="prompt output_prompt">Out[6]:</div>
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<pre>&lt;matplotlib.image.AxesImage at 0x106318c50&gt;</pre>
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"
>
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mfccs
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<h3 id="Feature-Scaling">Feature Scaling<a class="anchor-link" href="#Feature-Scaling">&#182;</a></h3>
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<p>Let's scale the MFCCs such that each coefficient dimension has zero mean and unit variance:</p>
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<div class="prompt input_prompt">In&nbsp;[7]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">mfccs</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">scale</span><span class="p">(</span><span class="n">mfccs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">print</span> <span class="n">mfccs</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">print</span> <span class="n">mfccs</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<pre>[ -4.64585635e-16  -1.63971401e-16  -1.09314267e-16  -1.09314267e-16
   0.00000000e+00   1.09314267e-16   0.00000000e+00  -1.09314267e-16
  -1.09314267e-16  -2.73285668e-17   1.09314267e-16  -8.19857003e-17
   5.46571335e-17   0.00000000e+00   2.73285668e-17  -4.09928501e-17
   1.09314267e-16   8.19857003e-17   9.56499837e-17   6.83214169e-17]
[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.]
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<p>Display the scaled MFCCs:</p>

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<div class="prompt input_prompt">In&nbsp;[8]:</div>
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<div class=" highlight hl-ipython2"><pre><span class="n">librosa</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">specshow</span><span class="p">(</span><span class="n">mfccs</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">x_axis</span><span class="o">=</span><span class="s">&#39;time&#39;</span><span class="p">)</span>
</pre></div>
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<pre>&lt;matplotlib.image.AxesImage at 0x1108b8890&gt;</pre>
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<h2 id="essentia.standard.MFCC"><code>essentia.standard.MFCC</code><a class="anchor-link" href="#essentia.standard.MFCC">&#182;</a></h2>
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<p>We can also use <a href="http://essentia.upf.edu/documentation/reference/std_MFCC.html"><code>essentia.standard.MFCC</code></a> to compute MFCCs across a signal, and we will display them as a "MFCC-gram":</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">hamming_window</span> <span class="o">=</span> <span class="n">ess</span><span class="o">.</span><span class="n">Windowing</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s">&#39;hamming&#39;</span><span class="p">)</span>
<span class="n">spectrum</span> <span class="o">=</span> <span class="n">ess</span><span class="o">.</span><span class="n">Spectrum</span><span class="p">()</span>  <span class="c"># we just want the magnitude spectrum</span>
<span class="n">mfcc</span> <span class="o">=</span> <span class="n">ess</span><span class="o">.</span><span class="n">MFCC</span><span class="p">(</span><span class="n">numberCoefficients</span><span class="o">=</span><span class="mi">13</span><span class="p">)</span>
<span class="n">frame_sz</span> <span class="o">=</span> <span class="mi">1024</span>
<span class="n">hop_sz</span> <span class="o">=</span> <span class="mi">500</span>

<span class="n">mfccs</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">mfcc</span><span class="p">(</span><span class="n">spectrum</span><span class="p">(</span><span class="n">hamming_window</span><span class="p">(</span><span class="n">frame</span><span class="p">)))[</span><span class="mi">1</span><span class="p">]</span>
               <span class="k">for</span> <span class="n">frame</span> <span class="ow">in</span> <span class="n">ess</span><span class="o">.</span><span class="n">FrameGenerator</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">frameSize</span><span class="o">=</span><span class="n">frame_sz</span><span class="p">,</span> <span class="n">hopSize</span><span class="o">=</span><span class="n">hop_sz</span><span class="p">)])</span>
<span class="k">print</span> <span class="n">mfccs</span><span class="o">.</span><span class="n">shape</span>
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<pre>(134, 13)
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<p>Scale the MFCCs:</p>

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<div class=" highlight hl-ipython2"><pre><span class="n">mfccs</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">scale</span><span class="p">(</span><span class="n">mfccs</span><span class="p">)</span>
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<pre>/usr/local/lib/python2.7/site-packages/sklearn/preprocessing/data.py:153: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.
  warnings.warn(&quot;Numerical issues were encountered &quot;
/usr/local/lib/python2.7/site-packages/sklearn/preprocessing/data.py:169: UserWarning: Numerical issues were encountered when scaling the data and might not be solved. The standard deviation of the data is probably very close to 0. 
  warnings.warn(&quot;Numerical issues were encountered &quot;
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<p>Plot the MFCCs:</p>

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<div class=" highlight hl-ipython2"><pre><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mfccs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s">&#39;lower&#39;</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s">&#39;auto&#39;</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s">&#39;nearest&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;MFCC Coefficient Index&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Frame Index&#39;</span><span class="p">)</span>
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<pre>&lt;matplotlib.text.Text at 0x1108cde10&gt;</pre>
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