{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCCGj4IglfH5"
      },
      "source": [
        "# **Taller 3 parte II**\n",
        "\n",
        "\n",
        "Entraremos ahora en la última parte de este taller, que introducirá el concepto de filtros y su utilización.\n",
        "\n",
        "Esta parte tiene un ejercicio obligatorio individual que también se entrega el miércoles 17 de abril a las 23:59hs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVRKlRmPmhYF"
      },
      "source": [
        "# Filtros\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_1un5sfjr9C"
      },
      "source": [
        "Un  filtro  es  un  bloque  de  procesamiento  que  modifica  de  alguna  forma  la  señal  que  ingresa  y  a  la  salida  devuelve la señal original modificada. En la figura siguiente x(t) es la señal de entrada e y(t) es la señal a la salida de aplicado el bloque.\n",
        "\n",
        "\n",
        "![Sistema.png](data:image/png;base64,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)\n",
        "\n",
        "\n",
        "  \n",
        "  En este taller, nos van a interesar los filtros que modifican el contenido en frecuencia de la señal de entrada. Así, por ejemplo, hablaremos de un filtro pasabajo si elimina las altas frecuencias o pasaalto si elimina las bajas frecuencias o pasabanda si solo deja pasar un rango de frecuencias.  \n",
        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        "\n",
        "\n",
        "A continuación se presentan tres funciones que representan los tres tipos de filtros mencionados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJA9V3fNlEMA"
      },
      "source": [
        "# frecuencia de corte es el punto en el que el filtro comenzará a atenuar o reducir la frecuencia.\n",
        "import numpy as np\n",
        "\n",
        "import wave\n",
        "import scipy.signal as signal\n",
        "def filtro_pasabajo(x,f_corte,fs):\n",
        "\ttaps = signal.firwin(151,f_corte,nyq=fs/2.0)\n",
        "\tfiltered_x = signal.lfilter(taps, 1.0, x)\n",
        "\treturn(filtered_x)\n",
        "\n",
        "def filtro_pasaalto(x,f_corte,fs):\n",
        "\ttaps = signal.firwin(151,f_corte, pass_zero=False,nyq=fs/2.0)\n",
        "\tfiltered_x = signal.lfilter(taps, 1.0, x)\n",
        "\treturn(filtered_x)\n",
        "\n",
        "def filtro_pasabanda(x,f_corte1,f_corte2,fs):\n",
        "\ttaps = signal.firwin(151, [f_corte1, f_corte2], pass_zero=False,nyq=fs/2.0)\n",
        "\tfiltered_x = signal.lfilter(taps, 1.0, x)\n",
        "\treturn(filtered_x)\n",
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ejercicio\n",
        "\n",
        "Construya una señal f(t) que sea la suma de tonos en las siguientes frecuencias 1 kHz, 5 kHz, 10 kHz, 15kHz; muestreados a una tasa de 48000 muestras por segundo. El largo de f(t) será de 0.1 segundo.\n",
        "\n",
        "Luego pase f(t) por un filtro pasabajo de frecuencia de corte 5 kHz y observe las componentes en frecuencia luego de filtrar la señal.  Haga lo mismo pasando dicha señal por un filtro pasalto de 5 kHz y por un pasabanda de frecuencias de corte 5 kHz y 10 kHz.  Observe lo que obtiene de los filtros y explique el resultado.\n"
      ],
      "metadata": {
        "id": "oplDjcQFRJDq"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5-GHhynlkBd"
      },
      "source": [
        "# celda para resolver el ejercicio anterior\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uVIAtJGOp0O8"
      },
      "source": [
        "# **Ejercicio 2 individual para entregar**\n",
        "a) Generar una señal suma de dos sinusoides una de amplitud 1 y frecuencia 100 Hz y otra de amplitud 0.1 y frecuencia 5 kHz con una tasa de muestreo de 40 kHz. Observar la señal en tiempo y frecuencia y explicar lo que observa en particular en el tiempo. Luego pasar dicha señal por un filtro pasabajo de 1 kHz de frecuencia de corte y observar la señal en el tiempo y frecuencia, comparando con la señal en tiempo y frecuencia antes de filtrar. Escuchar la señal antes y luego de filtrarla y explicar lo que escucha.\n",
        "\n",
        "b) Tomar el archivo wav que desee, o grabe su voz usando un micrófono de la PC o con el celular. Escuchar y observar la señal en tiempo y frecuencia. Luego filtrar de diferentes formas dicha señal, ver sus componentes en frecuencia luego de filtrar, escuchar dicha señal y explicar lo que se observa y escucha.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usYjaN-XiePm"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}