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<title>knn_instrument_classification</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">sklearn</span><span class="o">,</span> <span class="nn">librosa</span><span class="o">,</span> <span class="nn">mir_eval</span><span class="o">,</span> <span class="nn">urllib</span><span class="o">,</span> <span class="nn">IPython.display</span><span class="o">,</span> <span class="nn">stanford_mir</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#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">5</span><span class="p">)</span>
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</pre></div>

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<p><a href="index.html">&larr; Back to Index</a></p>
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<h1 id="Exercise:-Instrument-Classification-using-K-NN">Exercise: Instrument Classification using K-NN<a class="anchor-link" href="#Exercise:-Instrument-Classification-using-K-NN">&#182;</a></h1>
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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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<p>For more on K-NN, see the <a href="knn.ipynb">notebook on K-NN</a>.</p>
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<p>For help from a similar exercise, <a href="feature_sonification.ipynb#Step-1:-Retrieve-Audio">follow the steps in the feature sonification exercise</a> first.</p>
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<h2 id="Goals">Goals<a class="anchor-link" href="#Goals">&#182;</a></h2>
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</div>
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<ol>
<li>Extract spectral features from an audio signal.</li>
<li>Train a K-Nearest Neighbor classifier.</li>
<li>Use the classifier to classify beats in a drum loop.</li>
</ol>
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<h2 id="Step-1:-Retrieve-Audio,-Detect-Onsets,-and-Segment">Step 1: Retrieve Audio, Detect Onsets, and Segment<a class="anchor-link" href="#Step-1:-Retrieve-Audio,-Detect-Onsets,-and-Segment">&#182;</a></h2>
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</div>
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<p>Download the file <code>simple_loop.wav</code> onto your local machine.</p>
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<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
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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>
urllib.urlretrieve<span class="o">?</span>
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</pre></div>

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<p>Load the audio file:</p>
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<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span>librosa.load<span class="o">?</span>
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<p>Play the audio file:</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>Detect onsets:</p>
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<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span>librosa.onset.onset_detect<span class="o">?</span>
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<p>Convert onsets from frames to seconds (and samples):</p>
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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>Listen to a click track, with clicks located at each onset, plus the original audio:</p>
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<div class=" highlight hl-ipython2"><pre><span></span>mir_eval.sonify.clicks<span class="o">?</span>
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<h2 id="Step-2:-Extract-Features">Step 2: Extract Features<a class="anchor-link" href="#Step-2:-Extract-Features">&#182;</a></h2>
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<p>For each segment, compute the zero crossing rate and spectral centroid.</p>
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<p>Scale the features to be in the range [-1, 1]:</p>
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<h2 id="Step-3:-Train-K-NN-Classifier">Step 3: Train K-NN Classifier<a class="anchor-link" href="#Step-3:-Train-K-NN-Classifier">&#182;</a></h2>
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<p>Use <code>stanford_mir.download_drum_samples</code> to download ten kick drum samples and ten snare drum samples. Each audio file contains a single drum hit at the beginning of the file.</p>
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<div class=" highlight hl-ipython2"><pre><span></span>stanford_mir.download_drum_samples<span class="o">?</span>
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<p>For each audio file, extract one feature vector. Concatenate all of these feature vectors into one feature table.</p>
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<div class=" highlight hl-ipython2"><pre><span></span>numpy.concatenate<span class="o">?</span>
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<h2 id="Step-4:-Run-the-Classifier">Step 4: Run the Classifier<a class="anchor-link" href="#Step-4:-Run-the-Classifier">&#182;</a></h2>
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<p>Create a K-NN classifer model object:</p>
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<div class=" highlight hl-ipython2"><pre><span></span>sklearn.neighbors.KNeighborsClassifier<span class="o">?</span>
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<p>Train the classifier:</p>
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<p>Finally, run the classifier on the test input audio file:</p>
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<h2 id="Step-5:-Sonify-the-Classifier-Output">Step 5: Sonify the Classifier Output<a class="anchor-link" href="#Step-5:-Sonify-the-Classifier-Output">&#182;</a></h2>
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<p>Play a "beep" for each detected kick drum. Repeat for the snare drum.</p>
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<div class=" highlight hl-ipython2"><pre><span></span>mir_eval.sonify.clicks<span class="o">?</span>
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<h2 id="For-Further-Exploration">For Further Exploration<a class="anchor-link" href="#For-Further-Exploration">&#182;</a></h2>
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<p>In addition to the features used above, extract the following features (see librosa docs on <a href="http://librosa.github.io/librosa/feature.html">feature extraction</a>):</p>
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<ul>
<li>spectral centroid</li>
<li>spectral spread</li>
<li>spectral skewness</li>
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<li>spectral kurtosis</li>
<li>spectral rolloff</li>
<li>MFCCs</li>
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</ul>

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<p>Re-train the classifier, and re-run the classifier over the test audio signal. Do the results change?</p>

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<p>Repeat the steps above for more audio files.</p>
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spectral features; mfcc; knn instrument classification
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