pca; hpss; pitch transcription

index.html

@@ -271,6 +271,7 @@ div#notebook {
 <li><a href="zcr.html">Zero Crossing Rate</a> (<a href="zcr.ipynb">ipynb</a>)</li>
 <li><a href="spectral_features.html">Spectral Features</a> (<a href="spectral_features.ipynb">ipynb</a>)</li>
 <li><a href="mfcc.html">Mel-Frequency Cepstral Coefficients</a> (<a href="mfcc.ipynb">ipynb</a>)</li>
+<li><a href="pitch_transcription_exercise.html">Pitch Transcription Exercise</a> (<a href="pitch_transcription_exercise.ipynb">ipynb</a>)</li>
 </ol>
 
 </div>
@@ -293,9 +294,11 @@ div#notebook {
 <ol>
 <li><a href="basic_supervised_classification.html">Basic Supervised Classification</a> (<a href="basic_supervised_classification.ipynb">ipynb</a>)</li>
 <li><a href="knn.html">K-Nearest Neighbor Classification</a> (<a href="knn.ipynb">ipynb</a>)</li>
+<li><a href="cross_validation.html">Cross Validation</a> (<a href="cross_validation.ipynb">ipynb</a>)</li>
 <li><a href="knn_instrument_classification.html">Exercise: K-Nearest Neighbor Instrument Classification</a> (<a href="knn_instrument_classification.ipynb">ipynb</a>)</li>
 <li><a href="kmeans.html">K-Means Clustering</a> (<a href="kmeans.ipynb">ipynb</a>)</li>
 <li><a href="kmeans_instrument_classification.html">Exercise: Unsupervised Instrument Classification using K-Means</a> (<a href="kmeans_instrument_classification.ipynb">ipynb</a>)</li>
+<li><a href="notebooks/evaluation.ipynb">Evaluation</a></li>
 </ol>
 
 </div>
@@ -306,7 +309,7 @@ div#notebook {
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Chapter-4:-Matrix-Factorization-and-Evaluation">Chapter 4: Matrix Factorization and Evaluation<a class="anchor-link" href="#Chapter-4:-Matrix-Factorization-and-Evaluation">&#182;</a></h2>
+<h2 id="Chapter-4:-Matrix-Factorization">Chapter 4: Matrix Factorization<a class="anchor-link" href="#Chapter-4:-Matrix-Factorization">&#182;</a></h2>
 </div>
 </div>
 </div>
@@ -316,11 +319,11 @@ div#notebook {
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <ol>
+<li><a href="pca.html">Principal Component Analysis</a> (<a href="pca.ipynb">ipynb</a>)</li>
 <li><a href="nmf.html">Nonnegative Matrix Factorization</a> (<a href="nmf.ipynb">ipynb</a>)</li>
+<li><a href="hpss.html">Harmonic-Percussive Source Separation</a> (<a href="hpss.ipynb">ipynb</a>)</li>
 <li><a href="nmf_source_separation.html">Exercise: Source Separation using NMF</a> (<a href="nmf_source_separation.ipynb">ipynb</a>)</li>
 <li><a href="notebooks/classify_separated_signals.ipynb">Classification of Separated Signals</a></li>
-<li><a href="notebooks/cross_validation.ipynb">Cross Validation</a></li>
-<li><a href="notebooks/evaluation.ipynb">Evaluation</a></li>
 </ol>
 
 </div>

index.ipynb

@@ -66,7 +66,8 @@
     "1.  [Basic Feature Extraction](basic_feature_extraction.html) ([ipynb](basic_feature_extraction.ipynb))\n",
     "1.  [Zero Crossing Rate](zcr.html) ([ipynb](zcr.ipynb))\n",
     "1.  [Spectral Features](spectral_features.html) ([ipynb](spectral_features.ipynb))\n",
-    "1.  [Mel-Frequency Cepstral Coefficients](mfcc.html) ([ipynb](mfcc.ipynb))"
+    "1.  [Mel-Frequency Cepstral Coefficients](mfcc.html) ([ipynb](mfcc.ipynb))\n",
+    "1.  [Pitch Transcription Exercise](pitch_transcription_exercise.html) ([ipynb](pitch_transcription_exercise.ipynb))"
    ]
   },
   {
@@ -82,27 +83,29 @@
    "source": [
     "1.  [Basic Supervised Classification](basic_supervised_classification.html) ([ipynb](basic_supervised_classification.ipynb))\n",
     "1.  [K-Nearest Neighbor Classification](knn.html) ([ipynb](knn.ipynb))\n",
+    "1.  [Cross Validation](cross_validation.html) ([ipynb](cross_validation.ipynb))\n",
     "1.  [Exercise: K-Nearest Neighbor Instrument Classification](knn_instrument_classification.html) ([ipynb](knn_instrument_classification.ipynb))\n",
     "1.  [K-Means Clustering](kmeans.html) ([ipynb](kmeans.ipynb))\n",
-    "1.  [Exercise: Unsupervised Instrument Classification using K-Means](kmeans_instrument_classification.html) ([ipynb](kmeans_instrument_classification.ipynb))"
+    "1.  [Exercise: Unsupervised Instrument Classification using K-Means](kmeans_instrument_classification.html) ([ipynb](kmeans_instrument_classification.ipynb))\n",
+    "1.  [Evaluation](notebooks/evaluation.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Chapter 4: Matrix Factorization and Evaluation"
+    "## Chapter 4: Matrix Factorization"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "1.  [Principal Component Analysis](pca.html) ([ipynb](pca.ipynb))\n",
     "1.  [Nonnegative Matrix Factorization](nmf.html) ([ipynb](nmf.ipynb))\n",
+    "1.  [Harmonic-Percussive Source Separation](hpss.html) ([ipynb](hpss.ipynb))\n",
     "1.  [Exercise: Source Separation using NMF](nmf_source_separation.html) ([ipynb](nmf_source_separation.ipynb))\n",
-    "1.  [Classification of Separated Signals](notebooks/classify_separated_signals.ipynb)\n",
-    "1.  [Cross Validation](notebooks/cross_validation.ipynb)\n",
-    "1.  [Evaluation](notebooks/evaluation.ipynb)"
+    "1.  [Classification of Separated Signals](notebooks/classify_separated_signals.ipynb)\n"
    ]
   },
   {

notebooks/pitch_transcription_exercise.ipynb

-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:3a3278f7c6fb206a34581be477af529e901600f877d2eeeb0456448031912235"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Pitch Transcription Exercise"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Load an audio file."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import essentia.standard as ess\n",
-      "loader = ess.MonoLoader(filename='../test/test.wav', sampleRate=44100)\n",
-      "signal_in = loader()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Audio\n",
-      "#Audio(data=signal_in, rate=44100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Find each note and its pitch."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def get_onsets(signal_in):\n",
-      "    onset_times, onset_rate = ess.OnsetRate()(signal_in)\n",
-      "    return onset_times"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "yin = ess.PitchYinFFT()\n",
-      "spectrum = ess.Spectrum()\n",
-      "\n",
-      "def get_pitch(segment):\n",
-      "    if len(segment) < 4096:\n",
-      "        N = len(segment) if len(segment) % 2 == 0 else len(segment) - 1\n",
-      "    else:\n",
-      "        N = 4096\n",
-      "    pitch, pitch_conf = yin(spectrum(segment[:N]))\n",
-      "    return pitch"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def generate_sine(pitch, n_duration):\n",
-      "    #print midi, f0\n",
-      "    n = arange(n_duration)\n",
-      "    return 0.2*sin(2*pi*pitch*n/44100.0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def transcribe_pitch(signal_in):\n",
-      "    \n",
-      "    signal_out = zeros(len(signal_in))\n",
-      "    \n",
-      "    # get onsets\n",
-      "    onsets = get_onsets(signal_in)\n",
-      "    \n",
-      "    # get pitches\n",
-      "    for i in range(len(onsets)-1):\n",
-      "        n0 = int(onsets[i]*44100)\n",
-      "        n1 = int(onsets[i+1]*44100)\n",
-      "        pitch = get_pitch(signal_in[n0:n1])\n",
-      "        \n",
-      "        signal_out[n0:n1] = generate_sine(pitch, n1-n0)\n",
-      "    \n",
-      "    return signal_out\n",
-      "    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "signal_out = transcribe_pitch(signal_in)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Play the synthesized transcription."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import essentia\n",
-      "#Audio(data=signal_out, rate=44100)\n",
-      "signal_out = sum([signal_out, signal_in], axis=0)\n",
-      "ess.MonoWriter(filename='../test/out.wav')(essentia.array(signal_out))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file