{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Sounds as Vectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as pt\n",
        "\n",
        "from html5_audio import DEFAULT_RATE, get_html5_wave_player"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here's a function that produces a 'beep' with a parabola as an 'envelope':"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "def make_beep(freq, duration=0.3):\n",
        "    nsamples = DEFAULT_RATE * duration\n",
        "\n",
        "    t = np.linspace(0, duration, nsamples)\n",
        "    data = np.sin(freq*2*np.pi*t)\n",
        "\n",
        "    ramp = np.linspace(0, 1, nsamples)\n",
        "    data = data * (1-ramp)**2\n",
        "    \n",
        "    return data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's put one into the variable `beep`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "beep = make_beep(440)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "It's just a numpy array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(beep)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A pretty long one though:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(13230,)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "beep.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's take a look:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f460b6ba160>]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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IiSQM9JIsZLpIKgY+JhVDmEqSRNLVVR/o5aIxPhauqwirejioM5F0d2eKhJUC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\nkmJVSZpikP1DotJSQzEPQNuCQltTEBK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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f460b7830b8>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pt.plot(beep)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This consists of 44100 'samples' (=data points) per second of sound."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "        <audio controls=\"controls\" style=\"width:600px\" >\n",
              "          <source\n",
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              "/v/+//7//v/+//7//v/+//7//v/9//3//f/9//3//v/+//7//v/+//7//v/+//7//v/+////////\n",
              "//////////////8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAQABAAEA\n",
              "AQABAAEAAQABAAEAAQABAAEAAQABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=\n",
              "\" type=\"audio/wav\" />\n",
              "          Your browser does not support the audio element.\n",
              "        </audio>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.core.display.HTML at 0x7f460b6dec88>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_html5_wave_player(beep)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-----------------------\n",
        "Can we compose some simple 'music' with this? Let's shoot for three seconds..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "music = np.zeros(DEFAULT_RATE * 3, dtype=np.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "beep1 = make_beep(880, duration=0.8)\n",
        "beep2 = make_beep(880 * 1.5, duration=0.8)\n",
        "beep3 = make_beep(880 * 2, duration=0.8)\n",
        "l = len(beep1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "music.fill(0)\n",
        "music[0:l] += beep1\n",
        "s = int(0.3*DEFAULT_RATE)\n",
        "music[s:s+l] += 0.1*beep2\n",
        "s = int(0.6*DEFAULT_RATE)\n",
        "music[s:s+l] += 0.5*beep3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
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