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BabyCryAnalysis.html
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<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>BabyCryAnalysis</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
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<h1 id="Baby-Cry-Analysis-&-Testing">Baby Cry Analysis & Testing<a class="anchor-link" href="#Baby-Cry-Analysis-&-Testing">¶</a></h1>
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<h2 id="Part-1:-Let's-train-a-machine-learning-algorithm-and-test-it's-performance">Part 1: Let's train a machine learning algorithm and test it's performance<a class="anchor-link" href="#Part-1:-Let's-train-a-machine-learning-algorithm-and-test-it's-performance">¶</a></h2>
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<p>We will begin by collecting all of the sample audio files we have, chopping them into smaller audio snippets and training a collection of machine learning algorithms with part of this data. With another part of the data we will test and see how well the algorithms predict data they have never seen before and then choose the best algorithm for our project</p>
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<h3 id="Step-1:--Grab-the-audio-file-and-its-label-(we-have-3-labels:-hungry,-pain,-and-asphyxia)">Step 1: Grab the audio file and its label (we have 3 labels: hungry, pain, and asphyxia)<a class="anchor-link" href="#Step-1:--Grab-the-audio-file-and-its-label-(we-have-3-labels:-hungry,-pain,-and-asphyxia)">¶</a></h3>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Store all audio files in dictionary where key: filename, value: label</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="n">raw_audio</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">directory</span> <span class="o">=</span> <span class="s">'Full_hunger'</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">directory</span><span class="p">):</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="n">raw_audio</span><span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">filename</span><span class="p">)]</span> <span class="o">=</span> <span class="s">'hungry'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">directory</span> <span class="o">=</span> <span class="s">'Full_pain'</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">directory</span><span class="p">):</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="n">raw_audio</span><span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">filename</span><span class="p">)]</span> <span class="o">=</span> <span class="s">'pain'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">directory</span> <span class="o">=</span> <span class="s">'Full_asphyxia'</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">directory</span><span class="p">):</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="n">raw_audio</span><span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">filename</span><span class="p">)]</span> <span class="o">=</span> <span class="s">'asphyxia'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">continue</span>
<span class="c">#print raw_audio</span>
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<h1 id="If-you-are-having-issues-with-python-being-able-to-make-the-different-directories,-try-doing-it-manually-or-with-the-os.system-commands">If you are having issues with python being able to make the different directories, try doing it manually or with the os.system commands<a class="anchor-link" href="#If-you-are-having-issues-with-python-being-able-to-make-the-different-directories,-try-doing-it-manually-or-with-the-os.system-commands">¶</a></h1><p><code>os.system("sudo mkdir /audio")</code></p>
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<h2 id="Step-2:--Chop-the-audio-file-into-1-sec.-snippets-and-save-them-in-corresponding-folders">Step 2: Chop the audio file into 1 sec. snippets and save them in corresponding folders<a class="anchor-link" href="#Step-2:--Chop-the-audio-file-into-1-sec.-snippets-and-save-them-in-corresponding-folders">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">wave</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="k">def</span> <span class="nf">chop_song</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">folder</span><span class="p">):</span>
<span class="n">handle</span> <span class="o">=</span> <span class="n">wave</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s">'rb'</span><span class="p">)</span>
<span class="n">frame_rate</span> <span class="o">=</span> <span class="n">handle</span><span class="o">.</span><span class="n">getframerate</span><span class="p">()</span>
<span class="n">n_frames</span> <span class="o">=</span> <span class="n">handle</span><span class="o">.</span><span class="n">getnframes</span><span class="p">()</span>
<span class="n">window_size</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">frame_rate</span>
<span class="n">num_secs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">n_frames</span><span class="o">/</span><span class="n">frame_rate</span><span class="p">))</span>
<span class="c">#print filename</span>
<span class="n">last_number_frames</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c">#Slicing Audio file</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">num_secs</span><span class="p">):</span>
<span class="n">shortfilename</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">"/"</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">"."</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">snippetfilename</span> <span class="o">=</span> <span class="s">'audio/'</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s">'/'</span> <span class="o">+</span> <span class="n">shortfilename</span> <span class="o">+</span> <span class="s">'snippet'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s">'.wav'</span>
<span class="c">#print snippetfilename</span>
<span class="n">snippet</span> <span class="o">=</span> <span class="n">wave</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">snippetfilename</span> <span class="p">,</span><span class="s">'wb'</span><span class="p">)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setnchannels</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setsampwidth</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">getsampwidth</span><span class="p">())</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setframerate</span><span class="p">(</span><span class="n">frame_rate</span><span class="p">)</span>
<span class="c">#snippet.setsampwidth(2)</span>
<span class="c">#snippet.setframerate(11025)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setnframes</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">getnframes</span><span class="p">())</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">writeframes</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">readframes</span><span class="p">(</span><span class="n">window_size</span><span class="p">))</span>
<span class="n">handle</span><span class="o">.</span><span class="n">setpos</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">tell</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">frame_rate</span><span class="p">)</span>
<span class="c">#print snippetfilename, ":", snippet.getnchannels(), snippet.getframerate(), snippet.getnframes(), snippet.getsampwidth()</span>
<span class="c">#The last audio slice might be less than a second, if this is the case, we don't want to include it because it will not fit into our matrix </span>
<span class="k">if</span> <span class="n">last_number_frames</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">last_number_frames</span> <span class="o">=</span> <span class="n">snippet</span><span class="o">.</span><span class="n">getnframes</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">snippet</span><span class="o">.</span><span class="n">getnframes</span><span class="p">()</span> <span class="o">!=</span> <span class="n">last_number_frames</span><span class="p">:</span>
<span class="c">#print "this file doesnt have the same frame size!, remaming file"</span>
<span class="n">os</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">snippetfilename</span><span class="p">,</span> <span class="n">snippetfilename</span><span class="o">+</span><span class="s">".bak"</span><span class="p">)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c">#handle.close()</span>
<span class="k">for</span> <span class="n">audio_file</span> <span class="ow">in</span> <span class="n">raw_audio</span><span class="p">:</span>
<span class="n">chop_song</span><span class="p">(</span><span class="n">audio_file</span><span class="p">,</span> <span class="n">raw_audio</span><span class="p">[</span><span class="n">audio_file</span><span class="p">])</span>
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<h2 id="Step-3:--Transform-.wav-files-to-frequency-spectrum-"fingerprints"-using-MFCC-algorithm">Step 3: Transform .wav files to frequency spectrum "fingerprints" using MFCC algorithm<a class="anchor-link" href="#Step-3:--Transform-.wav-files-to-frequency-spectrum-"fingerprints"-using-MFCC-algorithm">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">librosa</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="sd">'''Chop and Transform each track'''</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">45</span><span class="p">),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s">'float32'</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">j</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">filename</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">'audio/pain/'</span><span class="p">)):</span>
<span class="n">last_number_frames</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="c">#print filename</span>
<span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</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">"audio/pain/"</span> <span class="o">+</span> <span class="n">filename</span><span class="p">)</span>
<span class="n">fingerprint</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">y</span><span class="o">=</span><span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span> <span class="n">n_mfcc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">fingerprint</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s">'float32'</span><span class="p">)</span>
<span class="n">x</span><span class="p">[</span><span class="mi">44</span><span class="p">]</span> <span class="o">=</span> <span class="s">'pain'</span>
<span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">j</span> <span class="o">=</span> <span class="n">i</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">filename</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">'audio/hungry/'</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="c">#print filename</span>
<span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</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">"audio/hungry/"</span> <span class="o">+</span> <span class="n">filename</span><span class="p">)</span>
<span class="n">fingerprint</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">y</span><span class="o">=</span><span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span> <span class="n">n_mfcc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">fingerprint</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s">'float32'</span><span class="p">)</span>
<span class="n">x</span><span class="p">[</span><span class="mi">44</span><span class="p">]</span> <span class="o">=</span> <span class="s">'hungry'</span>
<span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">k</span> <span class="o">=</span> <span class="n">i</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">filename</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">'audio/asphyxia/'</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="c">#print filename</span>
<span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</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">"audio/asphyxia/"</span> <span class="o">+</span> <span class="n">filename</span><span class="p">)</span>
<span class="n">fingerprint</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">y</span><span class="o">=</span><span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span> <span class="n">n_mfcc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">fingerprint</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s">'float32'</span><span class="p">)</span>
<span class="n">x</span><span class="p">[</span><span class="mi">44</span><span class="p">]</span> <span class="o">=</span> <span class="s">'asphyxia'</span>
<span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="n">j</span><span class="o">+</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c">#Do something with missing values. you might want to do something more sophisticated with missing values later</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class="output_area"><div class="prompt output_prompt">Out[16]:</div>
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<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
<th>1</th>
<th>2</th>
<th>3</th>
<th>4</th>
<th>5</th>
<th>6</th>
<th>7</th>
<th>8</th>
<th>9</th>
<th>...</th>
<th>34</th>
<th>35</th>
<th>36</th>
<th>37</th>
<th>38</th>
<th>39</th>
<th>40</th>
<th>41</th>
<th>42</th>
<th>43</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>61.164639</td>
<td>47.077801</td>
<td>-31.072571</td>
<td>-96.637245</td>
<td>-130.160461</td>
<td>-123.079628</td>
<td>-122.755928</td>
<td>-106.404770</td>
<td>-89.965424</td>
<td>-90.127457</td>
<td>...</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
</tr>
<tr>
<th>2</th>
<td>88.442520</td>
<td>76.313026</td>
<td>-2.145989</td>
<td>-30.406242</td>
<td>-61.038811</td>
<td>-67.060280</td>
<td>-54.365772</td>
<td>-55.774155</td>
<td>-47.985542</td>
<td>-15.517380</td>
<td>...</td>
<td>-86.546463</td>
<td>-30.136751</td>
<td>-7.350064</td>
<td>-10.286840</td>
<td>-12.592611</td>
<td>-13.583686</td>
<td>-21.501377</td>
<td>-11.565689</td>
<td>62.324951</td>
<td>69.309616</td>
</tr>
<tr>
<th>3</th>
<td>-29.781023</td>
<td>-60.232071</td>
<td>-96.828278</td>
<td>-90.998222</td>
<td>-94.779671</td>
<td>-105.075417</td>
<td>-97.519058</td>
<td>-99.080528</td>
<td>-105.163040</td>
<td>-106.028229</td>
<td>...</td>
<td>-90.644783</td>
<td>-83.937691</td>
<td>-80.022545</td>
<td>-91.994202</td>
<td>-102.968040</td>
<td>-90.376099</td>
<td>-89.551079</td>
<td>-84.076927</td>
<td>-11.199362</td>
<td>3.811244</td>
</tr>
<tr>
<th>4</th>
<td>-48.579533</td>
<td>-68.671791</td>
<td>-113.557213</td>
<td>-111.534363</td>
<td>-108.681320</td>
<td>-106.165474</td>
<td>-101.677551</td>
<td>-95.523392</td>
<td>-102.661011</td>
<td>-105.140739</td>
<td>...</td>
<td>-80.398064</td>
<td>-92.367661</td>
<td>-95.758377</td>
<td>-104.873161</td>
<td>-106.258102</td>
<td>-109.926643</td>
<td>-111.612045</td>
<td>-98.675140</td>
<td>-91.144066</td>
<td>-95.634537</td>
</tr>
<tr>
<th>5</th>
<td>10.824058</td>
<td>13.150772</td>
<td>-6.948591</td>
<td>-17.075489</td>
<td>-12.422709</td>
<td>-0.860229</td>
<td>3.998694</td>
<td>3.655787</td>
<td>-2.229299</td>
<td>-17.267118</td>
<td>...</td>
<td>-9.970703</td>
<td>-41.767918</td>
<td>-99.628151</td>
<td>-110.191643</td>
<td>-78.499588</td>
<td>-62.011497</td>
<td>-63.363140</td>
<td>-67.923515</td>
<td>-67.200996</td>
<td>-55.079521</td>
</tr>
</tbody>
</table>
<p>5 rows × 44 columns</p>
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<h2 id="Step-4:--Make-a-Test-Train-Split-of-the-data">Step 4: Make a Test-Train-Split of the data<a class="anchor-link" href="#Step-4:--Make-a-Test-Train-Split-of-the-data">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="mi">44</span><span class="p">]</span>
<span class="k">del</span> <span class="n">X</span><span class="p">[</span><span class="mi">44</span><span class="p">]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
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<h2 id="Step-5:--Fit-the-training-data-to-a-model-&-Check-the-models-performance-against-the-test-data¶">Step 5: Fit the training data to a model & Check the models performance against the test data¶<a class="anchor-link" href="#Step-5:--Fit-the-training-data-to-a-model-&-Check-the-models-performance-against-the-test-data¶">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="c">#Code to hide deprication warnings</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
<span class="n">HTML</span><span class="p">(</span><span class="s">'''<script></span>
<span class="s">code_show_err=false; </span>
<span class="s">function code_toggle_err() {</span>
<span class="s"> if (code_show_err){</span>
<span class="s"> $('div.output_stderr').hide();</span>
<span class="s"> } else {</span>
<span class="s"> $('div.output_stderr').show();</span>
<span class="s"> }</span>
<span class="s"> code_show_err = !code_show_err</span>
<span class="s">} </span>
<span class="s">$( document ).ready(code_toggle_err);</span>
<span class="s"></script></span>
<span class="s">To toggle on/off output_stderr, click <a href="javascript:code_toggle_err()">here</a>.'''</span><span class="p">)</span>
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<script>
code_show_err=false;
function code_toggle_err() {
if (code_show_err){
$('div.output_stderr').hide();
} else {
$('div.output_stderr').show();
}
code_show_err = !code_show_err
}
$( document ).ready(code_toggle_err);
</script>
To toggle on/off output_stderr, click <a href="javascript:code_toggle_err()">here</a>.
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">MultinomialNB</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span>
<span class="k">def</span> <span class="nf">get_scores</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">classifier</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_predict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> \
<span class="n">precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">),</span> \
<span class="n">recall_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">)</span>
<span class="k">print</span> <span class="s">" Model, Accuracy, Precision, Recall"</span>
<span class="k">print</span> <span class="s">" Random Forest:"</span><span class="p">,</span> <span class="n">get_scores</span><span class="p">(</span><span class="n">RandomForestClassifier</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="k">print</span> <span class="s">" Logistic Regression:"</span><span class="p">,</span> <span class="n">get_scores</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="k">print</span> <span class="s">" Decision Tree:"</span><span class="p">,</span> <span class="n">get_scores</span><span class="p">(</span><span class="n">DecisionTreeClassifier</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="k">print</span> <span class="s">" SVM:"</span><span class="p">,</span> <span class="n">get_scores</span><span class="p">(</span><span class="n">SVC</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="c">#print " Naive Bayes:", get_scores(MultinomialNB, X_train, X_test, y_train, y_test)</span>
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<pre> Model, Accuracy, Precision, Recall
Random Forest: </pre>
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<pre>//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1203: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1304: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1203: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
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<pre>(0.80693069306930698, 0.80875431729219438, 0.80693069306930698)
Logistic Regression: </pre>
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<pre>//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1304: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1203: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1304: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1203: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
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<pre>(0.70792079207920788, 0.72826645954770641, 0.70792079207920788)
Decision Tree: (0.7722772277227723, 0.77233616218764733, 0.7722772277227723)
SVM: (0.41584158415841582, 0.17292422311538083, 0.41584158415841582)
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<pre>//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1304: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring="f1_weighted" instead of scoring="f1".
sample_weight=sample_weight)
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<h2 id="Results-Model:-(Accuracy,-Precision,-Recall)">Results Model: (Accuracy, Precision, Recall)<a class="anchor-link" href="#Results-Model:-(Accuracy,-Precision,-Recall)">¶</a></h2><p>We tested four popular machine learning algorithms to see which ones had the most accuracte predictions with our test data, here are the results:</p>
<h4 id="Random-Forest:-~80%-Accuracy">Random Forest: ~80% Accuracy<a class="anchor-link" href="#Random-Forest:-~80%-Accuracy">¶</a></h4><h4 id="Logistic-Regression:-~70%-Accuracy">Logistic Regression: ~70% Accuracy<a class="anchor-link" href="#Logistic-Regression:-~70%-Accuracy">¶</a></h4><h4 id="Decision-Tree:-~77%-Accuracy">Decision Tree: ~77% Accuracy<a class="anchor-link" href="#Decision-Tree:-~77%-Accuracy">¶</a></h4><h4 id="Support-Vector-Machines:-~47%-Accuracy">Support Vector Machines: ~47% Accuracy<a class="anchor-link" href="#Support-Vector-Machines:-~47%-Accuracy">¶</a></h4><p>I am glossing over a lot of details here. Different algorithms have different performance speeds and settings that we can tweak to improve their accuracy, precision, and recall. Random Forests usually perform best with little tweaking although they aren't the fastest in most cases. For this experiment however, I think random forests are fine for building the basic version of our application</p>
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<h2 id="After-you-are-satisfied-with-the-results-of-your-model,-you-can-save-the-model-into-a-.pkl-file-that-you-can-quickly-use-to-make-predictions-of-new-data.--I-will-fit-a-new-random-forest-model-that-uses-all-of-the-data-I-have-and-save-it-as-'myRandomForest.pkl'">After you are satisfied with the results of your model, you can save the model into a .pkl file that you can quickly use to make predictions of new data. I will fit a new random forest model that uses all of the data I have and save it as 'myRandomForest.pkl'<a class="anchor-link" href="#After-you-are-satisfied-with-the-results-of-your-model,-you-can-save-the-model-into-a-.pkl-file-that-you-can-quickly-use-to-make-predictions-of-new-data.--I-will-fit-a-new-random-forest-model-that-uses-all-of-the-data-I-have-and-save-it-as-'myRandomForest.pkl'">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">cPickle</span>
<span class="k">def</span> <span class="nf">pickle_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">modelname</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s">'models/'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">modelname</span><span class="p">)</span> <span class="o">+</span> <span class="s">'.pkl'</span><span class="p">,</span> <span class="s">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="k">return</span> <span class="n">cPickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
<span class="n">pickle_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s">"myRandomForest"</span><span class="p">)</span>
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<h2 id="After-you-pkl-a-model-you-can-open-it-up-later-on-as-so.">After you pkl a model you can open it up later on as so.<a class="anchor-link" href="#After-you-pkl-a-model-you-can-open-it-up-later-on-as-so.">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">getModel</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="k">return</span> <span class="n">cPickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<h1 id="Part-2">Part 2<a class="anchor-link" href="#Part-2">¶</a></h1><h2 id="Let's-see-if-it-works!-Making-Actual-Predictions-on-new-sounds">Let's see if it works! Making Actual Predictions on new sounds<a class="anchor-link" href="#Let's-see-if-it-works!-Making-Actual-Predictions-on-new-sounds">¶</a></h2><p>I downloaded from YouTube an audio file of a hungry baby crying. (<a href="https://www.youtube.com/watch?v=n87mdkR4kIY">https://www.youtube.com/watch?v=n87mdkR4kIY</a>) I know our dataset probably isn't big enough to make a strong prediction but let's see if we can get an algorithm working that makes a prediction.</p>
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<h2 id="Step-1:-Load-the-model-from-disk-into-Python">Step 1: Load the model from disk into Python<a class="anchor-link" href="#Step-1:-Load-the-model-from-disk-into-Python">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="n">model</span> <span class="o">=</span> <span class="n">getModel</span><span class="p">(</span><span class="s">"models/myRandomForest.pkl"</span><span class="p">)</span>
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<h2 id="Step-2:--Chop-the-wav-file-and-store-it-in-a-folder">Step 2: Chop the wav file and store it in a folder<a class="anchor-link" href="#Step-2:--Chop-the-wav-file-and-store-it-in-a-folder">¶</a></h2><p>I should have done a better job making the old chop_songs method more reusable, oh well.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">def</span> <span class="nf">chop_new_audio</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">folder</span><span class="p">):</span>
<span class="n">handle</span> <span class="o">=</span> <span class="n">wave</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s">'rb'</span><span class="p">)</span>
<span class="n">frame_rate</span> <span class="o">=</span> <span class="n">handle</span><span class="o">.</span><span class="n">getframerate</span><span class="p">()</span>
<span class="n">n_frames</span> <span class="o">=</span> <span class="n">handle</span><span class="o">.</span><span class="n">getnframes</span><span class="p">()</span>
<span class="n">window_size</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">frame_rate</span>
<span class="n">num_secs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">n_frames</span><span class="o">/</span><span class="n">frame_rate</span><span class="p">))</span>
<span class="c">#print filename</span>
<span class="n">last_number_frames</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c">#Slicing Audio file</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">num_secs</span><span class="p">):</span>
<span class="n">shortfilename</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">"."</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">snippetfilename</span> <span class="o">=</span> <span class="n">folder</span> <span class="o">+</span> <span class="s">'/'</span> <span class="o">+</span> <span class="n">shortfilename</span> <span class="o">+</span> <span class="s">'snippet'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s">'.wav'</span>
<span class="c">#print snippetfilename</span>
<span class="n">snippet</span> <span class="o">=</span> <span class="n">wave</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">snippetfilename</span> <span class="p">,</span><span class="s">'wb'</span><span class="p">)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setnchannels</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setsampwidth</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">getsampwidth</span><span class="p">())</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setframerate</span><span class="p">(</span><span class="n">frame_rate</span><span class="p">)</span>
<span class="c">#snippet.setsampwidth(2)</span>
<span class="c">#snippet.setframerate(11025)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">setnframes</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">getnframes</span><span class="p">())</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">writeframes</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">readframes</span><span class="p">(</span><span class="n">window_size</span><span class="p">))</span>
<span class="n">handle</span><span class="o">.</span><span class="n">setpos</span><span class="p">(</span><span class="n">handle</span><span class="o">.</span><span class="n">tell</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">frame_rate</span><span class="p">)</span>
<span class="c">#print snippetfilename, ":", snippet.getnchannels(), snippet.getframerate(), snippet.getnframes(), snippet.getsampwidth()</span>
<span class="c">#The last audio slice might be less than a second, if this is the case, we don't want to include it because it will not fit into our matrix </span>
<span class="k">if</span> <span class="n">last_number_frames</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">last_number_frames</span> <span class="o">=</span> <span class="n">snippet</span><span class="o">.</span><span class="n">getnframes</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">snippet</span><span class="o">.</span><span class="n">getnframes</span><span class="p">()</span> <span class="o">!=</span> <span class="n">last_number_frames</span><span class="p">:</span>
<span class="c">#print "this file doesnt have the same frame size!, remaming file"</span>
<span class="n">os</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">snippetfilename</span><span class="p">,</span> <span class="n">snippetfilename</span><span class="o">+</span><span class="s">".bak"</span><span class="p">)</span>
<span class="n">snippet</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c">#handle.close()</span>
<span class="n">chop_new_audio</span><span class="p">(</span><span class="s">"babycryingformilk.wav"</span><span class="p">,</span> <span class="s">"babycryingformilk"</span><span class="p">)</span>
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<h2 id="Step-3:--Transform-the-chopped-snippets-into-MFCC-fingerprints-and-make-a-prediction">Step 3: Transform the chopped snippets into MFCC fingerprints and make a prediction<a class="anchor-link" href="#Step-3:--Transform-the-chopped-snippets-into-MFCC-fingerprints-and-make-a-prediction">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">filename</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s">'babycryingformilk/'</span><span class="p">)):</span>
<span class="n">last_number_frames</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s">".wav"</span><span class="p">):</span>
<span class="c">#print filename</span>
<span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</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">"babycryingformilk/"</span><span class="o">+</span><span class="n">filename</span><span class="p">)</span>
<span class="n">fingerprint</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">y</span><span class="o">=</span><span class="n">audiofile</span><span class="p">,</span> <span class="n">sr</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span> <span class="n">n_mfcc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">fingerprint</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="s">'float32'</span><span class="p">)</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">fingerprint</span><span class="p">)</span>
<span class="c">#print prediction</span>
<span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prediction</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<h2 id="Step-4:-Take-the-mode-of-the-predictions-to-come-up-with-a-final-predition">Step 4: Take the mode of the predictions to come up with a final predition<a class="anchor-link" href="#Step-4:-Take-the-mode-of-the-predictions-to-come-up-with-a-final-predition">¶</a></h2>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
<span class="k">print</span> <span class="n">data</span><span class="o">.</span><span class="n">most_common</span><span class="p">()</span> <span class="c"># Returns all unique items and their counts</span>
<span class="k">print</span> <span class="n">data</span><span class="o">.</span><span class="n">most_common</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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<pre>[('hungry', 29)]
[('hungry', 29)]
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<p>So our algorithm worked! (But maybe we just got lucky) I think when you build a bigger dataset and test with more sound files you will be able to see how well the algorithm really performs. There are also many ways to adjust the algorithm parameters and test how that affects the accuracy of predictions. I hope this is useful for getting started! Let me know if you have any other questions or concerns and I will be more than happy to help!</p>
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