Masterarbeit_Simulation/ANR_high_level.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Funktionen und Abhängigkeiten\n",
    "\n",
    "import time\n",
    "import os\n",
    "from ipywidgets import interact\n",
    "from scipy import signal\n",
    "from scipy.signal import savgol_filter\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import soundfile as sf\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "# Soundfile laden\n",
    "def load_wav(filename):\n",
    "    y, fs = sf.read(filename, dtype='float32')\n",
    "    return fs, y.T\n",
    "\n",
    "# Sensitivätskurve Mikrofon laden (normiert auf 1000 Hz)\n",
    "def load_transfer_function(filename):\n",
    "    df = pd.read_csv(filename, skiprows=3, header=None, dtype=float, sep=\";\")\n",
    "    frequencies = df.iloc[:, 0]\n",
    "    gain = df.iloc[:, 1]\n",
    "    return frequencies, gain\n",
    "\n",
    "# Transferfunktion für frequenzabhängige Veränderung von Signal anlegen\n",
    "def apply_transfer_function_freq(signal, fs, frequencies, gain_dB, delay_seconds=0):\n",
    "    # Signal in Frequenzbereich fouriertransformieren, Frequenzbins berechnen, linearen Gain berechnen\n",
    "    N = len(signal)\n",
    "    freq_signal = np.fft.rfft(signal)\n",
    "    freq_bins = np.fft.rfftfreq(N, d=1/fs)\n",
    "    gain_linear = 10 ** (gain_dB / 20.0)    \n",
    "    # Gain Werte interpolieren auf die tatsächlichen Frequenzbins\n",
    "    # Phasenshift für die Verzögerung berechnen\n",
    "    # Signalfrequenzen modifizieren. \n",
    "    # Signal wieder zurück in Zeitbereich\n",
    "    gain_interp = np.interp(freq_bins, frequencies, gain_linear)\n",
    "    phase_shift = np.exp(-1j * 2 * np.pi * freq_bins * delay_seconds)\n",
    "    modified_freq_signal = freq_signal * gain_interp * phase_shift \n",
    "    modified_signal = np.fft.irfft(modified_freq_signal, n=N)\n",
    "    return modified_signal\n",
    "\n",
    "# ANR Algorithmmus\n",
    "def anr_function(input, ref_noise, coefficients, mu, adaption_step = 1):\n",
    "    coefficient_matrix = np.zeros((len(input), coefficients), dtype=np.float32)\n",
    "    output=np.zeros(input.shape[0], dtype=np.float32)\n",
    "    filter = np.zeros(coefficients, dtype=np.float32)\n",
    "    \n",
    "    for j in range(0, len(input) - len(filter)): \n",
    "        accumulator=0\n",
    "        for i in range(coefficients):\n",
    "            noise=ref_noise[j+i]\n",
    "            accumulator+=filter[i] * noise\n",
    "        output[j] = input[j] - accumulator\n",
    "        corrector = mu * output[j]\n",
    "        if (j % adaption_step) == 0:\n",
    "            for k in range(coefficients):\n",
    "                filter[k] += corrector*ref_noise[j+k]  \n",
    "        coefficient_matrix[j, :] = filter[:]\n",
    "    return output, coefficient_matrix\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für Simple Usecases\n",
    "\n",
    "SIMULATION = False\n",
    "AUDIO = False\n",
    "PLOT = False\n",
    "\n",
    "plot = 'sine_1'\n",
    "\n",
    "# Chirp Generator\n",
    "n=2000 #Sampleanzahl\n",
    "fs=20000 #Samplingrate\n",
    "f0=100 #Startfrequenz\n",
    "f1=1000 #Stopfrequenz\n",
    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
    "if plot == 'sine_1':\n",
    "    f_disturber=2000 #Störfrequenz\n",
    "else:\n",
    "    f_disturber=500 #Störfrequenz\n",
    "\n",
    "signal_amplitude=0.5\n",
    "disturber_amplitude=0.25\n",
    "\n",
    "# Parameter setzen\n",
    "coefficients = 16\n",
    "step_size = 0.01\n",
    "indices = [0, coefficients // 2, coefficients - 1]\n",
    "\n",
    "t = np.linspace(0, t1, n)\n",
    "\n",
    "# Zielsignal anlegen\n",
    "desired_signal = signal.chirp(t, f0=f0, f1=f1, t1=t1, method='linear')*signal_amplitude\n",
    "\n",
    "# Störsignal anlegen\n",
    "if plot == 'sine_1' or plot == 'sine_2':\n",
    "    noise_signal = np.sin(2*np.pi*f_disturber*t) * disturber_amplitude\n",
    "else:\n",
    "    noise_signal = np.random.normal(0, 1, n) * disturber_amplitude\n",
    "\n",
    "# Korruptes Zielsignal anlegen\n",
    "corrupted_signal = desired_signal + noise_signal\n",
    "\n",
    "# Zeitachse anlegen, ANR Algorithmus ausführen\n",
    "t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/1000\n",
    "output, coefficient_matrix = anr_function(corrupted_signal, noise_signal, coefficients, step_size, adaption_step=1)\n",
    "\n",
    "# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "error_signal = (output - desired_signal)[:-coefficients]\n",
    "t2 = np.linspace(0, len(error_signal), len(error_signal))/1000\n",
    "\n",
    "# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "snr_before = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(noise_signal**2, t))\n",
    "snr_after = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(error_signal**2, t2))\n",
    "delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "if SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    desired_signal_simulation = desired_signal*(2**(15)-1)\n",
    "    noise_signal_simulation = noise_signal*(2**(15)-1)\n",
    "    corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/simple_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
    "    np.savetxt('simulation_data/simple_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/simple_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "\n",
    "# Plots des Filterprozesses\n",
    "figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
    "ax0.set_ylim(-1, 1)\n",
    "ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
    "ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
    "ax2.plot(t, noise_signal, c='chocolate', label='Reference noise signal')\n",
    "ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
    "\n",
    "ax0.text(0.5, -0.3, '(a) Desired signal',\n",
    "         transform=ax0.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.set_xlabel('time(s)', x=0.05)\n",
    "ax0.set_ylabel('Amplitude')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax3.set_ylabel('Amplitude')\n",
    "\n",
    "# Plots der Filterperfomanz\n",
    "figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax4.set_ylim(-1, 1)\n",
    "ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
    "for i in indices:\n",
    "    ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(a) Error signal',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel('time(s)', x=0.05)\n",
    "ax4.set_ylabel('Amplitude')\n",
    "ax5.set_ylabel('Coeffcient value')\n",
    "\n",
    "#Grids direkt auf Subplots anwenden\n",
    "ax0.grid(True, linestyle='--', alpha=0.4)\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax0.spines['top'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax0.spines['right'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_plot_1_{plot}', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_plot_2_{plot}', dpi=600)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für intermediate Usecases\n",
    "\n",
    "SIMULATION = False\n",
    "AUDIO = False\n",
    "PLOT = False\n",
    "\n",
    "# Chirp Generator\n",
    "n=2000 #Sampleanzahl\n",
    "fs=20000 #Samplingrate\n",
    "f0=100 #Startfrequenz\n",
    "f1=1000 #Stopfrequenz\n",
    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
    "f_disturber=2000 #Störfrequenz\n",
    "\n",
    "# .wav File laden, Tonspuren den Signalen zuordnen, Corrputed Target Signal erstellen\n",
    "fs, data_1 = load_wav(f'./audio_data/Nutzsignal/male.wav')\n",
    "fs, data_2 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
    "\n",
    "# Signale laden und zuordnen\n",
    "desired_signal = data_1\n",
    "noise_signal = data_2\n",
    "corrupted_signal = desired_signal + noise_signal\n",
    "\n",
    "# Parameter setzen\n",
    "coefficients = 16\n",
    "step_size = 0.01\n",
    "indices = [0, coefficients // 2, coefficients - 1]\n",
    "\n",
    "# Zeitachse anlegen, ANR Algorithmus ausführen\n",
    "t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/10000\n",
    "output, coefficient_matrix = anr_function(corrupted_signal, noise_signal, coefficients, step_size, adaption_step=1)\n",
    "output = output * max(desired_signal) / max(output)\n",
    "\n",
    "if AUDIO == True:\n",
    "    # Audiodateien zum Vergleich abspeichern\n",
    "    sf.write('corrupted_signal.wav', corrupted_signal, fs)\n",
    "    sf.write('filter_output.wav', output, fs)\n",
    "\n",
    "# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "error_signal = (output - desired_signal)[:-coefficients]\n",
    "t2 = np.linspace(0, len(error_signal), len(error_signal))/10000\n",
    "\n",
    "# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "snr_before = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(noise_signal**2, t))\n",
    "snr_after = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(error_signal**2, t2))\n",
    "delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "if SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    desired_signal_simulation = desired_signal*(2**(15)-1)\n",
    "    noise_signal_simulation = noise_signal*(2**(15)-1)\n",
    "    corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/intermediate_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
    "    np.savetxt('simulation_data/intermediate_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/intermediate_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "\n",
    "# Plots des Filterprozesses\n",
    "figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
    "ax0.set_ylim(-1, 1)\n",
    "ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
    "ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
    "ax2.plot(t, noise_signal, c='chocolate', label='Reference noise signal')\n",
    "ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
    "\n",
    "ax0.text(0.5, -0.3, '(a) Desired signal',\n",
    "         transform=ax0.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.set_xlabel('time(s)', x=0.05)\n",
    "ax0.set_ylabel('Amplitude')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax3.set_ylabel('Amplitude')\n",
    "\n",
    "# Plots der Filterperfomanz\n",
    "figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax4.set_ylim(-1, 1)\n",
    "ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
    "for i in indices:\n",
    "    ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(a) Error signal',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel('time(s)', x=0.05)\n",
    "ax4.set_ylabel('Amplitude')\n",
    "ax5.set_ylabel('Coeffcient value')\n",
    "\n",
    "#Grids direkt auf Subplots anwenden\n",
    "ax0.grid(True, linestyle='--', alpha=0.4)\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax0.spines['top'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax0.spines['right'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_plot_1_wav', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_plot_2_wav', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für komplexen Usecases\n",
    "\n",
    "from pandas import Series\n",
    "\n",
    "\n",
    "SIMULATION = True\n",
    "AUDIO = False\n",
    "PLOT = False\n",
    "SERIES = False\n",
    "\n",
    "# Chirp Generator\n",
    "n=2000 #Sampleanzahl\n",
    "fs=20000 #Samplingrate\n",
    "f0=100 #Startfrequenz\n",
    "f1=1000 #Stopfrequenz\n",
    "t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
    "f_disturber=2000 #Störfrequenz\n",
    "\n",
    "# Parameter setzen\n",
    "coefficients = 16\n",
    "step_size = 0.01\n",
    "delay_r11 = 0.000\n",
    "delay_vpu = 0.002\n",
    "indices = [0, coefficients // 2, coefficients - 1]\n",
    "\n",
    "# .wav File laden, Tonspuren den Signalen zuordnen, Corrputed Target Signal erstellen, Reduced Noise Signal erstellen\n",
    "fs, data_1 = load_wav(f'./audio_data/Nutzsignal/male.wav')\n",
    "fs, data_2 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
    "\n",
    "# Sensitivätskurve Mikrofon laden (normiert auf 1000 Hz)\n",
    "frequency_r11, gain_r11 = load_transfer_function('./transfer_functions/R11_normalized.csv')\n",
    "frequency_vpu, gain_vpu = load_transfer_function('./transfer_functions/VPU17BA01_normlized.csv')\n",
    "\n",
    "# Signale laden und zuordnen\n",
    "desired_signal = data_1\n",
    "noise_signal = data_2\n",
    "desired_signal_r11 = apply_transfer_function_freq(desired_signal, fs, frequency_r11, gain_r11, delay_r11)\n",
    "noise_signal_r11 = apply_transfer_function_freq(noise_signal, fs, frequency_r11, gain_r11, delay_r11)\n",
    "noise_signal_vpu = apply_transfer_function_freq(noise_signal, fs, frequency_vpu, gain_vpu, delay_vpu)\n",
    "corrupted_signal = desired_signal_r11 + noise_signal_r11\n",
    "\n",
    "# Zeitachse anlegen, ANR Algorithmus ausführen\n",
    "t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/10000\n",
    "\n",
    "if SERIES == True:\n",
    "    for i in range(16, coefficients+2, 2):\n",
    "        output, coefficient_matrix = anr_function(corrupted_signal, noise_signal_vpu, i, step_size, adaption_step=1)\n",
    "\n",
    "        # Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "        coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "        error_signal = (output - desired_signal_r11)[:-coefficients]\n",
    "        t2 = np.linspace(0, len(error_signal), len(error_signal))\n",
    "\n",
    "        # SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "        snr_before = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(noise_signal_vpu**2, t))\n",
    "        snr_after = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))\n",
    "        delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "        with open('snr_evaluation/male+drinking', 'a', newline='') as f:\n",
    "            writer = csv.writer(f)\n",
    "            writer.writerow([i, delta_snr])\n",
    "else:\n",
    "        output, coefficient_matrix = anr_function(corrupted_signal, noise_signal_vpu, coefficients, step_size, adaption_step=1)\n",
    "\n",
    "# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
    "coefficient_matrix = coefficient_matrix[:-coefficients]\n",
    "error_signal = (output - desired_signal_r11)[:-coefficients]\n",
    "t2 = np.linspace(0, len(error_signal), len(error_signal))/10000\n",
    "\n",
    "# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
    "snr_before = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(noise_signal_vpu**2, t))\n",
    "snr_after = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))\n",
    "delta_snr = round(snr_after - snr_before, 2)\n",
    "\n",
    "if AUDIO == True:\n",
    "    # Audiodateien zum Vergleich abspeichern\n",
    "    sf.write('corrupted_signal.wav', corrupted_signal, fs)\n",
    "    sf.write('filter_output.wav', output, fs)\n",
    "\n",
    "if SIMULATION == True:\n",
    "    # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
    "    desired_signal_simulation = desired_signal*(2**(15)-1)\n",
    "    noise_signal_simulation = noise_signal*(2**(15)-1)\n",
    "    corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
    "    np.savetxt('simulation_data/complex_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
    "    np.savetxt('simulation_data/complex_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "    np.savetxt('simulation_data/complex_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
    "\n",
    "# Plots des Filterprozesses\n",
    "figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
    "ax0.set_ylim(-1, 1)\n",
    "ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
    "ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
    "ax2.plot(t, noise_signal_vpu, c='chocolate', label='Reference noise signal')\n",
    "ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
    "\n",
    "ax0.text(0.5, -0.3, '(a) Desired signal',\n",
    "         transform=ax0.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.set_xlabel('time(s)', x=0.05)\n",
    "ax0.set_ylabel('Amplitude')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax3.set_ylabel('Amplitude')\n",
    "\n",
    "# Plots der Filterperfomanz\n",
    "figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax4.set_ylim(-1, 1)\n",
    "ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
    "for i in indices:\n",
    "    ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(a) Error signal',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel('time(s)', x=0.05)\n",
    "ax4.set_ylabel('Amplitude')\n",
    "ax5.set_ylabel('Coeffcient value')\n",
    "\n",
    "# Plot Sensitivitätskurve\n",
    "figure3, (ax6, ax7) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
    "ax6.set_ylim(min(gain_r11), max(gain_r11))\n",
    "ax7.set_ylim(min(gain_vpu), max(gain_vpu))\n",
    "ax6.plot(frequency_r11, gain_r11, c='indianred', label='Sensitivity Curve (Primary sensor)' )\n",
    "ax7.plot(frequency_vpu, gain_vpu, c='orangered', label='Sensitivity Curve (Secondary sensor)')\n",
    "\n",
    "ax6.text(0.5, -0.3, '(a) Sensitivity Curve (Primary sensor)',\n",
    "         transform=ax6.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "ax7.text(0.5, -0.5, '(b) Sensitivity Curve (Secondary sensor)',\n",
    "         transform=ax7.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax7.set_xlabel('Frequency (Hz)', x=0.1)\n",
    "ax6.set_ylabel('Gain (dB)')\n",
    "ax7.set_ylabel('Gain (dB)')\n",
    "\n",
    "# Plot für Störsignalvergleich\n",
    "figure4, (ax8, ax9, ax10) = plt.subplots(3, 1, figsize=(15, 10), sharex=True, sharey=True)\n",
    "ax8.set_ylim(1.0, -1.0)\n",
    "ax8.plot(t, noise_signal, c='orange', label='Noise signal')\n",
    "ax9.plot(t, noise_signal_r11, c='darkorange', label='Corruption noise signal (Primary sensor)')\n",
    "ax10.plot(t, noise_signal_vpu, c='peru', label='Reference noise signal (Secondary sensor)')\n",
    "\n",
    "ax8.text(0.5, -0.3, '(a) Noise signal',\n",
    "         transform=ax8.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax9.text(0.5, -0.3, '(b) Corruption noise signal (Primary sensor)',\n",
    "         transform=ax9.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax10.text(0.5, -0.5, '(c) Reference noise signal (Secondary sensor)',\n",
    "         transform=ax10.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax10.set_xlabel('time(s)', x=0.05)\n",
    "ax8.set_ylabel('Amplitude')\n",
    "ax9.set_ylabel('Amplitude')\n",
    "ax10.set_ylabel('Amplitude')\n",
    "\n",
    "#Grids direkt auf Subplots anwenden\n",
    "ax0.grid(True, linestyle='--', alpha=0.4)\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "ax6.grid(True, linestyle='--', alpha=0.4)\n",
    "ax7.grid(True, linestyle='--', alpha=0.4)\n",
    "ax8.grid(True, linestyle='--', alpha=0.4)\n",
    "ax9.grid(True, linestyle='--', alpha=0.4)\n",
    "ax10.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax0.spines['top'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax6.spines['top'].set_visible(False)\n",
    "ax7.spines['top'].set_visible(False)\n",
    "ax8.spines['top'].set_visible(False)\n",
    "ax9.spines['top'].set_visible(False)\n",
    "ax10.spines['top'].set_visible(False)\n",
    "ax0.spines['right'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "ax6.spines['right'].set_visible(False)\n",
    "ax7.spines['right'].set_visible(False)\n",
    "ax8.spines['right'].set_visible(False)\n",
    "ax9.spines['right'].set_visible(False)\n",
    "ax10.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "figure2.tight_layout()\n",
    "figure3.tight_layout()\n",
    "figure4.tight_layout()\n",
    "if PLOT == True:\n",
    "    figure1.savefig(f'plots/fig_plot_1_wav_complex', dpi=600)\n",
    "    figure2.savefig(f'plots/fig_plot_2_wav_complex', dpi=600)\n",
    "    figure3.savefig(f'plots/fig_plot_3_wav_complex', dpi=600)\n",
    "    figure4.savefig(f'plots/fig_plot_4_wav_complex', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots für SNR Vergleich\n",
    "\n",
    "PLOT = True\n",
    "\n",
    "# Daten aus .csv laden\n",
    "data_male_breathing = np.loadtxt('snr_evaluation/male+breathing', delimiter=\",\")\n",
    "data_male_chewing = np.loadtxt('snr_evaluation/male+chewing', delimiter=\",\")\n",
    "data_male_scratching = np.loadtxt('snr_evaluation/male+scratching', delimiter=\",\")\n",
    "data_male_drinking = np.loadtxt('snr_evaluation/male+drinking', delimiter=\",\")\n",
    "data_male_coughing = np.loadtxt('snr_evaluation/male+coughing', delimiter=\",\")\n",
    "\n",
    "\n",
    "# Daten laden\n",
    "x = data_male_breathing[:, 0]\n",
    "male_breathing = savgol_filter(data_male_breathing[:, 1], 10, 3)\n",
    "male_chewing = savgol_filter(data_male_chewing[:, 1], 10, 3)\n",
    "male_scratching = savgol_filter(data_male_scratching[:, 1], 10, 3)\n",
    "male_drinking = savgol_filter(data_male_drinking[:, 1], 10, 3)\n",
    "male_coughing = savgol_filter(data_male_coughing[:, 1], 10, 3)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.plot(x, male_breathing, linestyle='-',  linewidth=1.5, label='Breathing Noise')\n",
    "plt.plot(x, male_chewing, linestyle='--', linewidth=1.5, label='Chewing Noise')\n",
    "plt.plot(x, male_scratching, linestyle='-.', linewidth=1.5, label='Scratching Noise')\n",
    "plt.plot(x, male_drinking, linestyle=':',  linewidth=1.5, label='Drinking Noise')\n",
    "plt.plot(x, male_coughing, linestyle=(0, (3, 1, 1, 1)), linewidth=1.5, label='Coughing Noise')\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 25     # Legende\n",
    "})\n",
    "\n",
    "plt.xlabel(\"Number of filter coefficients\")\n",
    "plt.ylabel(\"SNR-Gain (dB)\")\n",
    "plt.grid(True, linestyle='--', alpha=0.4)\n",
    "#Spines auf ganzen Plot anwenden\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "plt.tight_layout()\n",
    "if PLOT == True:\n",
    "    plt.savefig(f'plots/fig_snr_comparison', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_6624\\2320771910.py:96: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  figure1.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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AWUEJlAyzvWYq9Va2PXiyXHvR9lYK0htbPV/qHcQqzHqtRssZpNe9phm0t5l6TQn1RQEAaVPaa7gW9dJVT9dKNcPV+3Xnzp3e60GOu5rnyZMnTSNKj9dxj7Ohns9aZt3dVkr/r+f9xmupd73VGgOmVntJy6Vpl/emtmVB/Hq9azntstnvUO/3V09x9ShXb+9Ky1Fem1sxWT6fpAfE1HdQu0/rvNKyadtqPVRqGwZZ3xpgXdOuNIaN5lfps1H+HgEAALKCBHiG2bqTlWpK6zl78uF38he0VIqtR11remH4fUYNeZ142ca933t1i6hu84zqds16apBr+WqdcOqEQyfaQW+XtreichsqACBt7DEqCCUOlcyrVu7CHj+DXMjWMbHRMm+12jRROnLkSNWkbK2a5vWuN71f28bWOS9nt1u110unUy87j0a+v/1+lT5v28BRdX6Ia0BMbUN9T7/EvNrsismwg9cHqYdfqX55lL9HAACArCABnmFqTKvxqt4l6oFs2d42lU5M1VhW4902mHViUloLs/R9mo59n5LR1d6nR+n81ZCv9t7SOo52WaudyOq7qRe0XlfPmPKTDj2vhr2+Z6N1GUtt3brV+6u6i0HZ3jfVkvVK0uuEIsw0o6ppDgBA1EprGfvR8U/HRb/BItVm0aO0LVFr3o0ovbAcdzLc7zhu22mVvk+j602dIGybr5zaXjb5XOl1W2e7GltrOs7vL3bZKnUEsL2yG70bIM4BMe24L2obBllP2uaV1kW19W3Xn+6C9Fv2uH+PAAAAWUACPOOUUFXyVQ8lidXItoPalFOjvb293XuPGs166ORCpVJKb73U63qfeojY9+kEQ+/T86WNc31Oz6nxbd+rBrN9r6XGtv5fz2ue9r2ah563va4rNdx1S6uSx/pO+rzeq4ee03dqZCCsSrRcOhHRCWIQeq8dIEq36eo7afm0Hu1D0wxzu7g96fM7QQEAIElBeoDrQnuQcl72+Fird3fQXudB5iVxl3motxxdo+vNJmrt3XTlbUf7fKW7CNWOC5Po9RN3OT5Xg77bjg52QMwg7Lq1HSv86E7G0s+E6Qyj9qrWc7UEeuk0o/49AgAAZMWspBcAjVPyNUgPaDWAgzSsK50sVaOkbhA6abg4XmR9atVAjJpO/Gxv7lon26W9unVCEWb9+U1T8426liUAAFFQz+kgPcDVA1Z0bPS7C8om7jTdqHofV2PvktMx3o63US8ttx7VejrXW8as0fWm9ojtlFCp7ed3sf/EiROR3VnXSBk3fdbeUVgeE/Z7u7pTTvNXElztQ3XAUJu0VvvQbsMgF23se+xnglJ8qL2q7ajl0sNuW3WiKG9HRv17BAAAyAoS4EAF9oRBJ45RllcJSicyfjXcAQBIWpjyIUrM+SVDg9YbPnv2rImCjrE6xoet6VxtkMu4NLLelKRVr2C1KWy7Rv+2JTmUzNXyl76uxKftjZw0e6efapWXd4Kw2y1s3exGaBl0MUFJZN3tF0WHh3L1lOTRcmiZ1Ltb60X/1ja1pW70enkSO6rfIwAAQFZQAgWocaLjmq3R7vKkDgCAMNT7O0x5hKhKKUQ1HXuMtT2466Ue5EFKXCTxfW0ZNVv2wv7b9pq2Se/S19XuScvdZ2qHqSe7tlXperADj6uDguuxUuoZEDPINrTvCXJXRSVaT0paKx51x6X+ajtqGbdt21bXMgEAAOQJCXCgCp1I6KS40d5h9cxXJy1x9igDAKAROkYF6a1qj2VRDjYZVR3wegY2LBdXiYgo1puSopqOLXOipGd5gtXWkLbU7klL+0PLoiS+ejCrx7W2kx6293USvZTDDIgZZhvaOxvCrHutn2rLYEvy6SKBeoSXDyga9+CvAAAAaUMCHKhCJ7Q6cXDZE9uepNgeRgAApJGtoV2LTdAFqW0cpCd2lAla9XbWtHTcredit03IxiGq9VZa+kSlRMqX1/YSVzkYzSst5U/s9lEPb1vGw45lo3+77vldbUBMvyS8Xdd+tbYtu53DxlOtuLXLZ2Mk6t8jAABAVpAABwL0Aq80SFQc1KvJnlgBAJBWSpQGSZDpQrKSzEFKigW54Kx5RllyxA46PTg4GGoAQiUe1Ys2rkRsVOvNJjw1HX1X9QyulCBXUlmlUFwOOF6LLrCcPHnSybzUAztoWRA7IKb4/QbsNqzVhtR8FU/aFuXbpxbNv1bcahnsRaOof48AAABZQQIcqOHRRx/1EtNx10vUQFW6NTmJQTcBAAjDJuqCJMGVXNX7dJyrRglAJaFr0XT6+vpMVJQMVL1k/dV01RO6Fr3H9kSOUxTrzSY//QbrtLWiNa8gF+BtuY64y2hoeyjRHObCRL3UxgvzfWx98loUJ1qvfnGlRLPWe7W7/2qtb79Etf1epReNovw9AgAAZAUJcCDgwEJDQ0OxzUMndy5OpgEAiILt+RwkOanjqJLMOs5VStaph6ymU6v3sU22R93rWonhiYkJ7wK0ekz39PR4CcvyC982KajvUut4bdeL3/qp9Z6o1pvtBW7LnVR7PWjC024Hv4sfUXx/fS8lhpUIb2pquuzR3t7uPa/10miCXOtR21vrIUzZjyDl6hSr2oZazkoJZ81Tvdyfeuqpqhcfaq1vbbdK8aH4VdtVsVo67ajiCgAAIEuapjVUOICadOKiE+Qgt42GoROUbdu2eScoaRl4CgCAWpSAVM/SMMdFJdxsb2Q7IKOSdEGS2joO79+/30xNTZm46JisWtm2l6x6z2o5lUDUMiph6Xes1jrR50qT5/qskom2bEaQ90S53jQvvVdJz2qU9Nfrfj3Aqy23EsG2lEqU319JWG1vKV/ntjyKTX5r/mE7Eeizle4m0LIEjTHFQ9DtYJP19rvY7VIt0Vxrfet5rR/bS97+Du021Ouap1+8NhJXAAAAWUICHAhBJ986SbAnelHQiYZOXkh+AwCyRL1mdfzSxWEX1NPV3pWFfFMvZJWfs+VpqrFJXhuLlJEDAABAJSTAAQAAEJqSjypFoQR43BdxXc4LyVOZE/VoDlqGQz2x1XM/zrsDAAAAkF3UAAcAAEBoKrWgO6Jc9MhWD1/1/ib5nX8qySFhynDo7oC4BysHAABAdpEABwAAQF2U/FbP27iTj+oNHGTQQWRfaY3soE6cOOFbvxwAAADFRgIcAAAAdScrVabCDlYYZ+9vPVCcmFJZkyAXVjQApGKECyQAAACohhrgAAAAaEhPT48ZHx+PvESJEqB9fX3eYIj08C0WDW6pwTB1l0G1wcc1OLnuDtAjTMkUAAAAFAsJcAAAADRE5SqGhoa8RHWUVNtZCVB6fxeTLoCod7dKnHR0dFz22uTkpBkeHq6aHAcAAAAsEuAAAACIZPDCsbExrzduVD2A+/v7SXACAAAAaAgJcAAAAERC9Zil0R7b6lGuB2UtAAAAADSKBDgAAAAAAAAAIJdmJL0AAAAAAAAAAADEgQQ4AAAAAAAAACCXSIADAAAAAAAAAHKJBDgAAAAAAAAAIJdIgAMAAAAAAAAAcokEOAAAAAAAAAAgl0iAAwAAAAAAAAByiQQ4AAAAAAAAACCXSIADAAAAAAAAAEwe/X++GS0IC5tMFAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plots der Störsignale\n",
    "\n",
    "PLOT = True\n",
    "\n",
    "fs, data_1 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
    "fs, data_2 = load_wav(f'./audio_data/Störsignal/coughing.wav')\n",
    "fs, data_3 = load_wav(f'./audio_data/Störsignal/scratching.wav')\n",
    "fs, data_4 = load_wav(f'./audio_data/Störsignal/drinking.wav')\n",
    "fs, data_5 = load_wav(f'./audio_data/Störsignal/chewing.wav')\n",
    "\n",
    "t = np.linspace(0, len(data_1), len(data_1))/10000\n",
    "\n",
    "figure1, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(15, 15), sharex=True, sharey=True)\n",
    "ax1.set_ylim(1.0, -1.0)\n",
    "ax1.plot(t, data_1, c='darkorange', label='Breathing Noise')\n",
    "ax2.plot(t, data_2, c='indianred', label='Coughing Noise')\n",
    "ax3.plot(t, data_3, c='deepskyblue', label='Scratching Noise')\n",
    "ax4.plot(t, data_4, c='forestgreen', label='Drinking Noise')\n",
    "ax5.plot(t, data_5, c='darkorchid', label='Chewing Noise')\n",
    "\n",
    "ax1.text(0.5, -0.3, '(a) Breathing Noise',\n",
    "         transform=ax1.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax2.text(0.5, -0.3, '(b) Coughing Noise',\n",
    "         transform=ax2.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax3.text(0.5, -0.3, '(c) Scratching Noise',\n",
    "         transform=ax3.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax4.text(0.5, -0.3, '(d) Drinking Noise',\n",
    "         transform=ax4.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.text(0.5, -0.5, '(e) Chewing Noise',\n",
    "         transform=ax5.transAxes,\n",
    "         fontsize=25,\n",
    "         fontweight='normal',\n",
    "         ha='center',\n",
    "         va='bottom')\n",
    "\n",
    "ax5.set_xlabel(\"time (s)\", x=0.05)\n",
    "ax1.set_ylabel(\"Amplitude\")\n",
    "ax2.set_ylabel(\"Amplitude\")\n",
    "ax3.set_ylabel(\"Amplitude\")\n",
    "ax4.set_ylabel(\"Amplitude\")\n",
    "ax5.set_ylabel(\"Amplitude\")\n",
    "#ax5.xaxis.set_label_coords(0.5, -0.4)\n",
    "\n",
    "ax1.grid(True, linestyle='--', alpha=0.4)\n",
    "ax2.grid(True, linestyle='--', alpha=0.4)\n",
    "ax3.grid(True, linestyle='--', alpha=0.4)\n",
    "ax4.grid(True, linestyle='--', alpha=0.4)\n",
    "ax5.grid(True, linestyle='--', alpha=0.4)\n",
    "\n",
    "#Spines direkt auf Subplots anwenden\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax4.spines['top'].set_visible(False)\n",
    "ax5.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax4.spines['right'].set_visible(False)\n",
    "ax5.spines['right'].set_visible(False)\n",
    "\n",
    "# Schriftgrößen für LaTeX-Dokument\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    'font.size': 16,          # Standardtext\n",
    "    'axes.labelsize': 30,     # Achsenbeschriftungen\n",
    "    'xtick.labelsize': 25,    # Tick-Beschriftungen\n",
    "    'ytick.labelsize': 25,\n",
    "    'legend.fontsize': 15     # Legende\n",
    "})\n",
    "\n",
    "figure1.tight_layout()\n",
    "if PLOT == True:\n",
    "    plt.savefig(f'plots/fig_noise_signals', dpi=600)\n",
    "figure1.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
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