llliiu/HW4.ipynb

## HW4.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# MAT 201A Winter 2016 \n",
    "# HW 3 \n",
    "# Lu Liu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "from scipy.io import wavfile\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "sr, src = wavfile.read('glockenspiel.wav')\n",
    "sr2, src2 = wavfile.read('piano.wav')\n",
    "sr3, src3 = wavfile.read('tom.wav')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Fsrc = abs(fft.rfft(src, src.size)) / (src.size/2)# Normalize the frequency scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Fsrc2 = abs(fft.rfft(src2, src2.size)) / (src2.size/2)\n",
    "Fsrc3 = abs(fft.rfft(src3, src3.size)) / (src3.size/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1062a69d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmVkTG15hubMjYqukNwErJW0kCxKF+v1rQu3t7XvSbW1ttLW19XcVe3hQysxaUS6XI5fL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\n4IBgZmaJA4KZmQEOCGZmljggmJkZAP8fl5aQOTvNo1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10608e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(Fsrc)  \n",
    "title('Normalized frequency scale 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x106414f10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0vOy6SB9uy4BHgNmkK9OaXhPphN/TwKukcxpnkE5216UOYGdgbm6/D9h3gDV1\nAqvz7/lD5CtNyqypYvpvySeVS36dhgDX5m08CHykBWp6H+mqoaXAvaTgbFpNEeEb08zMLNlhTiqb\nmVnvHAhmZgY4EMzMLHMgmJkZ4EAwM7PMgWBmZoADwczMMgeCmZkB8P8BPtTQgpcp96cAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063d7410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(Fsrc2)  \n",
    "title('Normalized frequency scale 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10658f990>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hmJlVQ+mBYGZm1eBAMDMzwIFgZmZZ6YHgcwhmZtVQeiCYmVk1lB4I7iGYmVVDj4Eg6RBJ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\nEdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10652c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(Fsrc3) \n",
    "title('Normalized frequency scale 3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1323.3428349313294"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argmax(Fsrc) * sr / src.size # the frequency of the most prominent peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.057861328125"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argmax(Fsrc2) * sr2 / src2.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.565431458927179"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argmax(Fsrc3) * sr3/ src3.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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