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<title>feature_sonification</title>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">seaborn</span>
<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">librosa</span><span class="o">,</span> <span class="nn">IPython.display</span><span class="o">,</span> <span class="nn">mir_eval</span><span class="o">,</span> <span class="nn">urllib</span>
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<h1 id="Exercise:-Understanding-Audio-Features-through-Sonification">Exercise: Understanding Audio Features through Sonification<a class="anchor-link" href="#Exercise:-Understanding-Audio-Features-through-Sonification">&#182;</a></h1>
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<div class=" highlight hl-ipython2"><pre><span></span>plt.plot<span class="o">?</span>
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<p>that is a cue to use a particular command, in this case, <code>plot</code>. Run the cell to see documentation for that command. (To quickly close the Help window, press <code>q</code>.)</p>
<p>For more documentation, visit the links in the Help menu above. Also see the other notebooks; all the exercises here are covered somewhere else in separate notebooks.</p>

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<p>This exercise is loosely based upon "Lab 1" from previous MIR workshops (<a href="https://ccrma.stanford.edu/workshops/mir2010/Lab1_2010.pdf">2010</a>).</p>

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<h2 id="Goals">Goals<a class="anchor-link" href="#Goals">&#182;</a></h2>
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<p>In this exercise, you will segment, feature extract, and analyze audio files.</p>

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<ol>
<li>Detect onsets in an audio signal.</li>
<li>Segment the audio signal at each onset.</li>
<li>Compute features for each segment.</li>
<li>Gain intuition into the features by listening to each segment separately.</li>
</ol>

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<h2 id="Step-1:-Retrieve-Audio">Step 1: Retrieve Audio<a class="anchor-link" href="#Step-1:-Retrieve-Audio">&#182;</a></h2>
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<p>Download the file <code>simpleLoop.wav</code> onto your local machine.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;simple_loop.wav&#39;</span>
<span class="c1">#filename = &#39;125_bounce.wav&#39;</span>
<span class="c1">#filename = &#39;conga_groove.wav&#39;</span>
<span class="c1">#filename = &#39;58bpm.wav&#39;</span>
<span class="n">url</span> <span class="o">=</span> <span class="s1">&#39;http://audio.musicinformationretrieval.com/&#39;</span> <span class="o">+</span> <span class="n">filename</span>
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<div class=" highlight hl-ipython2"><pre><span></span>urllib.urlretrieve<span class="o">?</span>
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<p>Make sure the download worked:</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="o">%</span><span class="k">ls</span> *.wav
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</pre></div>

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<p>Save the audio signal into an array.</p>

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<div class=" highlight hl-ipython2"><pre><span></span>librosa.load<span class="o">?</span>
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<p>Show the sample rate:</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">print</span> <span class="n">fs</span>
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<p>Listen to the audio signal.</p>

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<div class=" highlight hl-ipython2"><pre><span></span>IPython.display.Audio<span class="o">?</span>
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<p>Display the audio signal.</p>

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<div class=" highlight hl-ipython2"><pre><span></span>librosa.display.waveplot<span class="o">?</span>
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<p>Compute the short-time Fourier transform:</p>

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<div class=" highlight hl-ipython2"><pre><span></span>librosa.stft<span class="o">?</span>
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<p>For display purposes, compute the log amplitude of the STFT:</p>

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<div class=" highlight hl-ipython2"><pre><span></span>librosa.logamplitude<span class="o">?</span>
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<p>Display the spectrogram.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Play with the parameters, including x_axis and y_axis</span>
librosa.display.specshow<span class="o">?</span>
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<h2 id="Step-2:-Detect-Onsets">Step 2: Detect Onsets<a class="anchor-link" href="#Step-2:-Detect-Onsets">&#182;</a></h2>
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<p>Find the times, in seconds, when onsets occur in the audio signal.</p>

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<div class=" highlight hl-ipython2"><pre><span></span>librosa.onset.onset_detect<span class="o">?</span>
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<div class=" highlight hl-ipython2"><pre><span></span>librosa.frames_to_time<span class="o">?</span>
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<p>Convert the onset times into sample indices.</p>

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<div class=" highlight hl-ipython2"><pre><span></span>librosa.frames_to_samples<span class="o">?</span>
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<p>Play a "beep" at each onset.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Use the `length` parameter so the click track is the same length as the original signal</span>
mir_eval.sonify.clicks<span class="o">?</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Play the click track &quot;added to&quot; the original signal</span>
IPython.display.Audio<span class="o">?</span>
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<h2 id="Step-3:-Segment-the-Audio">Step 3: Segment the Audio<a class="anchor-link" href="#Step-3:-Segment-the-Audio">&#182;</a></h2>
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<p>Save into an array, <code>segments</code>, 100-ms segments beginning at each onset.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Assuming these variables exist:</span>
<span class="c1"># x: array containing the audio signal</span>
<span class="c1"># fs: corresponding sampling frequency</span>
<span class="c1"># onset_samples: array of onsets in units of samples</span>
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<span class="n">frame_sz</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.100</span><span class="o">*</span><span class="n">fs</span><span class="p">)</span>
<span class="n">segments</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">x</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">frame_sz</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">onset_samples</span><span class="p">])</span>
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<p>Here is a function that adds 300 ms of silence onto the end of each segment and concatenates them into one signal.</p>
<p>Later, we will use this function to listen to each segment, perhaps sorted in a different order.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">concatenate_segments</span><span class="p">(</span><span class="n">segments</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="mi">44100</span><span class="p">,</span> <span class="n">pad_time</span><span class="o">=</span><span class="mf">0.300</span><span class="p">):</span>
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    <span class="n">padded_segments</span> <span class="o">=</span> <span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">segment</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">pad_time</span><span class="o">*</span><span class="n">fs</span><span class="p">))])</span> <span class="k">for</span> <span class="n">segment</span> <span class="ow">in</span> <span class="n">segments</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">padded_segments</span><span class="p">)</span>
<span class="n">concatenated_signal</span> <span class="o">=</span> <span class="n">concatenate_segments</span><span class="p">(</span><span class="n">segments</span><span class="p">,</span> <span class="n">fs</span><span class="p">)</span>
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<p>Listen to the newly concatenated signal.</p>

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<h2 id="Step-4:-Extract-Features">Step 4: Extract Features<a class="anchor-link" href="#Step-4:-Extract-Features">&#182;</a></h2>
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<p>For each segment, compute the zero crossing rate.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># returns a boolean array</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># you&#39;ll need this to actually count the number of zero crossings per segment</span>
sum<span class="o">?</span>
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<p>Use <code>argsort</code> to find an index array, <code>ind</code>, such that <code>segments[ind]</code> is sorted by zero crossing rate.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># zcrs: array, number of zero crossings in each frame</span>
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<span class="n">ind</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">zcrs</span><span class="p">)</span>
<span class="k">print</span> <span class="n">ind</span>
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<p>Sort the segments by zero crossing rate, and concatenate the sorted segments.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">concatenated_signal</span> <span class="o">=</span> <span class="n">concatenate_segments</span><span class="p">(</span><span class="n">segments</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">fs</span><span class="p">)</span>
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<h2 id="Step-5:-Listen-to-Segments">Step 5: Listen to Segments<a class="anchor-link" href="#Step-5:-Listen-to-Segments">&#182;</a></h2>
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<p>Listen to the sorted segments. What do you hear?</p>

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<div class=" highlight hl-ipython2"><pre><span></span>IPython.display.Audio<span class="o">?</span>
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<h2 id="More-Exercises">More Exercises<a class="anchor-link" href="#More-Exercises">&#182;</a></h2>
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<p>Repeat the steps above for the following audio files:</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#url = &#39;http://audio.musicinformationretrieval.com/125_bounce.wav&#39;</span>
<span class="c1">#url = &#39;http://audio.musicinformationretrieval.com/conga_groove.wav&#39;</span>
<span class="c1">#url = &#39;http://audio.musicinformationretrieval.com/58bpm.wav&#39;</span>
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