1130 lines
367 KiB
Plaintext
1130 lines
367 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Funktionen und Abhängigkeiten\n",
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"\n",
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"import time\n",
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"import os\n",
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"from ipywidgets import interact\n",
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"from scipy import signal\n",
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"from scipy.signal import savgol_filter\n",
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"from scipy.stats import pearsonr\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import soundfile as sf\n",
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"import pandas as pd\n",
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"import csv\n",
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"\n",
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"# Soundfile laden\n",
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"def load_wav(filename):\n",
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" y, fs = sf.read(filename, dtype='float32')\n",
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" return fs, y.T\n",
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"\n",
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"# Sensitivätskurve Mikrofon laden (normiert auf 1000 Hz)\n",
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"def load_transfer_function(filename):\n",
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" df = pd.read_csv(filename, skiprows=3, header=None, dtype=float, sep=\";\")\n",
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" frequencies = df.iloc[:, 0]\n",
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" gain = df.iloc[:, 1]\n",
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" return frequencies, gain\n",
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"\n",
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"# Transferfunktion für frequenzabhängige Veränderung von Signal anlegen\n",
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"def apply_transfer_function_freq(signal, fs, frequencies, gain_dB, delay_seconds=0):\n",
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" # Signal in Frequenzbereich fouriertransformieren, Frequenzbins berechnen, linearen Gain berechnen\n",
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" N = len(signal)\n",
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" freq_signal = np.fft.rfft(signal)\n",
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" freq_bins = np.fft.rfftfreq(N, d=1/fs)\n",
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" gain_linear = 10 ** (gain_dB / 20.0) \n",
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" # Gain Werte interpolieren auf die tatsächlichen Frequenzbins\n",
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" # Phasenshift für die Verzögerung berechnen\n",
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" # Signalfrequenzen modifizieren. \n",
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" # Signal wieder zurück in Zeitbereich\n",
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" gain_interp = np.interp(freq_bins, frequencies, gain_linear)\n",
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" phase_shift = np.exp(-1j * 2 * np.pi * freq_bins * delay_seconds)\n",
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" modified_freq_signal = freq_signal * gain_interp * phase_shift \n",
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" modified_signal = np.fft.irfft(modified_freq_signal, n=N)\n",
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" return modified_signal\n",
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"\n",
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"# ANR Algorithmmus\n",
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"def anr_function(input, ref_noise, coefficients, mu, adaption_step = 1):\n",
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" coefficient_matrix = np.zeros((len(input), coefficients), dtype=np.float32)\n",
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" output=np.zeros(input.shape[0], dtype=np.float32)\n",
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" filter = np.zeros(coefficients, dtype=np.float32)\n",
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" adaption_step = 1\n",
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" \n",
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" for j in range(0, len(input) - len(filter)): \n",
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" accumulator=0\n",
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" for i in range(coefficients):\n",
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" noise=ref_noise[j+i]\n",
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" accumulator+=filter[i] * noise\n",
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" output[j] = input[j] - accumulator\n",
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" corrector = mu * output[j]\n",
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" if (j % adaption_step) == 0:\n",
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" for k in range(coefficients):\n",
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" filter[k] += corrector*ref_noise[j+k]\n",
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" coefficient_matrix[j, :] = filter[:]\n",
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" return output, coefficient_matrix\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Plots für Simple Usecases\n",
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"\n",
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"SIMULATION = True\n",
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"AUDIO = False\n",
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"PLOT = False\n",
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"\n",
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"plot = 'sine_1'\n",
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"\n",
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"# Chirp Generator\n",
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"n=2000 #Sampleanzahl\n",
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"fs=20000 #Samplingrate\n",
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"f0=100 #Startfrequenz\n",
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"f1=1000 #Stopfrequenz\n",
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"t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
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"if plot == 'sine_1':\n",
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" f_disturber=2000 #Störfrequenz\n",
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"else:\n",
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" f_disturber=500 #Störfrequenz\n",
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"\n",
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"signal_amplitude=0.5\n",
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"disturber_amplitude=0.25\n",
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"\n",
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"# Parameter setzen\n",
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"coefficients = 16\n",
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"step_size = 0.01\n",
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"delay_r11 = 0.000\n",
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"delay_vpu = 0.000\n",
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"indices = [0, coefficients // 2, coefficients - 1]\n",
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"\n",
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"t = np.linspace(0, t1, n)\n",
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"\n",
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"# Zielsignal anlegen\n",
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"desired_signal = signal.chirp(t, f0=f0, f1=f1, t1=t1, method='linear')*signal_amplitude\n",
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"\n",
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"# Störsignal anlegen\n",
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"if plot == 'sine_1' or plot == 'sine_2':\n",
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" noise_signal = np.sin(2*np.pi*f_disturber*t) * disturber_amplitude\n",
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"else:\n",
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" noise_signal = np.random.normal(0, 1, n) * disturber_amplitude\n",
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"\n",
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"# Sensitivätskurve Mikrofon laden (normiert auf 1000 Hz)\n",
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"frequency_r11, gain_r11 = load_transfer_function('./transfer_functions/R11_normalized.csv')\n",
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"frequency_vpu, gain_vpu = load_transfer_function('./transfer_functions/VPU17BA01_normlized.csv')\n",
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"\n",
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"desired_signal_r11 = apply_transfer_function_freq(desired_signal, fs, frequency_r11, gain_r11, delay_r11)\n",
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"noise_signal_r11 = apply_transfer_function_freq(noise_signal, fs, frequency_r11, gain_r11, delay_r11)\n",
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"noise_signal_vpu = apply_transfer_function_freq(noise_signal, fs, frequency_vpu, gain_vpu, delay_vpu)\n",
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"corrupted_signal = desired_signal_r11 + noise_signal_r11\n",
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"\n",
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"# Zeitachse anlegen, ANR Algorithmus ausführen\n",
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"t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/1000\n",
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"output, coefficient_matrix = anr_function(corrupted_signal, noise_signal_vpu, coefficients, step_size, adaption_step=1)\n",
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"\n",
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"# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
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"coefficient_matrix = coefficient_matrix[:-coefficients]\n",
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"error_signal = (output - desired_signal_r11)[:-coefficients]\n",
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"t2 = np.linspace(0, len(error_signal), len(error_signal))/20000\n",
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"\n",
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"# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
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"snr_before = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(noise_signal_r11**2, t))\n",
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"snr_after = 10 * np.log10(np.trapz(desired_signal_r11**2, t) / np.trapz(error_signal**2, t2))\n",
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"delta_snr = round(snr_after - snr_before, 2)\n",
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"\n",
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"if SIMULATION == True:\n",
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" # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
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" dsp_desired_signal_r11 = desired_signal_r11*(2**(15)-1)\n",
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" dsp_noise_signal_r11 = noise_signal_r11*(2**(15)-1)\n",
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" dsp_noise_signal_vpu = noise_signal_vpu*(2**(15)-1)\n",
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" dsp_corrupted_signal = dsp_desired_signal_r11 + dsp_noise_signal_r11\n",
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" python_output = output*(2**(15)-1)\n",
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" np.savetxt('simulation_data/simple_dsp_desired_signal_r11.txt', dsp_desired_signal_r11, fmt='%d')\n",
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" np.savetxt('simulation_data/simple_dsp_noise_signal_r11.txt', dsp_noise_signal_r11, fmt='%d')\n",
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" np.savetxt('simulation_data/simple_dsp_noise_signal_vpu.txt', dsp_noise_signal_vpu, fmt='%d', delimiter=\"\\n\")\n",
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" np.savetxt('simulation_data/simple_dsp_corrupted_signal.txt', dsp_corrupted_signal, fmt='%d', delimiter=\"\\n\")\n",
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" np.savetxt('filter_output/simple_python_output.txt', python_output, fmt='%d', delimiter=\"\\n\")\n",
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" np.savetxt('filter_output/simple_python_filter_coefficients.txt', coefficient_matrix, fmt='%.4f', delimiter=\",\")\n",
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"\n",
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"# Plots des Filterprozesses\n",
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"figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
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"ax0.set_ylim(-1, 1)\n",
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"ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
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"ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
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"ax2.plot(t, noise_signal, c='chocolate', label='Reference noise signal')\n",
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"ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
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"\n",
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"ax0.text(0.5, -0.3, '(a) Desired signal',\n",
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" transform=ax0.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
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" transform=ax1.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax2.text(0.5, -0.3, '(c) Reference noise signal',\n",
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" transform=ax2.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax3.text(0.5, -0.5, f'(d) Filter output (SNR Gain = {delta_snr} dB)',\n",
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" transform=ax3.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax3.set_xlabel('time(s)', x=0.05)\n",
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"ax0.set_ylabel('Amplitude')\n",
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"ax1.set_ylabel('Amplitude')\n",
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"ax2.set_ylabel('Amplitude')\n",
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"ax3.set_ylabel('Amplitude')\n",
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"\n",
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"# Plots der Filterperfomanz\n",
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"figure2, (ax4, ax5) = plt.subplots(2, 1, figsize=(15, 7), sharex=True)\n",
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"ax4.set_ylim(-1, 1)\n",
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"ax4.plot(t2, error_signal, c='purple', label='Error (Desired signal - Filter output)')\n",
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"for i in indices:\n",
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" ax5.plot(t2, coefficient_matrix[:,i], label=f'Coefficient {i+1}')\n",
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"\n",
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"ax4.text(0.5, -0.3, '(a) Error signal',\n",
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" transform=ax4.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax5.text(0.5, -0.5, '(b) Coefficient values (1st, 8th, 16th)',\n",
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" transform=ax5.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax5.set_xlabel('time(s)', x=0.05)\n",
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"ax4.set_ylabel('Amplitude')\n",
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"ax5.set_ylabel('Coeffcient value')\n",
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"\n",
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"#Grids direkt auf Subplots anwenden\n",
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"ax0.grid(True, linestyle='--', alpha=0.4)\n",
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"ax1.grid(True, linestyle='--', alpha=0.4)\n",
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"ax2.grid(True, linestyle='--', alpha=0.4)\n",
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"ax3.grid(True, linestyle='--', alpha=0.4)\n",
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"ax4.grid(True, linestyle='--', alpha=0.4)\n",
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"ax5.grid(True, linestyle='--', alpha=0.4)\n",
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"\n",
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"#Spines direkt auf Subplots anwenden\n",
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"ax0.spines['top'].set_visible(False)\n",
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"ax1.spines['top'].set_visible(False)\n",
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"ax2.spines['top'].set_visible(False)\n",
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"ax3.spines['top'].set_visible(False)\n",
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"ax4.spines['top'].set_visible(False)\n",
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"ax5.spines['top'].set_visible(False)\n",
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"ax0.spines['right'].set_visible(False)\n",
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"ax1.spines['right'].set_visible(False)\n",
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"ax2.spines['right'].set_visible(False)\n",
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"ax3.spines['right'].set_visible(False)\n",
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"ax4.spines['right'].set_visible(False)\n",
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"ax5.spines['right'].set_visible(False)\n",
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"\n",
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"# Schriftgrößen für LaTeX-Dokument\n",
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"plt.rcParams.update({\n",
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" \"text.usetex\": True,\n",
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" \"font.family\": \"serif\",\n",
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" 'font.size': 16, # Standardtext\n",
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" 'axes.labelsize': 30, # Achsenbeschriftungen\n",
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" 'xtick.labelsize': 25, # Tick-Beschriftungen\n",
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" 'ytick.labelsize': 25,\n",
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" 'legend.fontsize': 15 # Legende\n",
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"})\n",
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"\n",
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"figure1.tight_layout()\n",
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"figure2.tight_layout()\n",
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"if PLOT == True:\n",
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" figure1.savefig(f'plots/fig_plot_1_{plot}', dpi=600)\n",
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" figure2.savefig(f'plots/fig_plot_2_{plot}', dpi=600)\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Plots für intermediate Usecases\n",
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"\n",
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"SIMULATION = False\n",
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"AUDIO = False\n",
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"PLOT = True\n",
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"\n",
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"# Chirp Generator\n",
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"n=2000 #Sampleanzahl\n",
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"fs=20000 #Samplingrate\n",
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"f0=100 #Startfrequenz\n",
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"f1=1000 #Stopfrequenz\n",
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"t1=n/fs #Chirpdauer (Samples/Samplingrate)\n",
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"f_disturber=2000 #Störfrequenz\n",
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"\n",
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"# .wav File laden, Tonspuren den Signalen zuordnen, Corrputed Target Signal erstellen\n",
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"fs, data_1 = load_wav(f'./audio_data/Nutzsignal/male.wav')\n",
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"fs, data_2 = load_wav(f'./audio_data/Störsignal/breathing.wav')\n",
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"\n",
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"# Signale laden und zuordnen\n",
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"desired_signal = data_1\n",
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"noise_signal = data_2\n",
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"corrupted_signal = desired_signal + noise_signal\n",
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"\n",
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"# Parameter setzen\n",
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"coefficients = 16\n",
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"step_size = 0.01\n",
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"indices = [0, coefficients // 2, coefficients - 1]\n",
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"\n",
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"# Zeitachse anlegen, ANR Algorithmus ausführen\n",
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"t = np.linspace(0, len(corrupted_signal), len(corrupted_signal))/20000\n",
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"output, coefficient_matrix = anr_function(corrupted_signal, noise_signal, coefficients, step_size, adaption_step=1)\n",
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"output = output * max(desired_signal) / max(output)\n",
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"\n",
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"if AUDIO == True:\n",
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" # Audiodateien zum Vergleich abspeichern\n",
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" sf.write('corrupted_signal.wav', corrupted_signal, fs)\n",
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" sf.write('filter_output.wav', output, fs)\n",
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"\n",
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"# Koeffizientenmatrix und Vergleich um Koeffizientenanzahl kürzen, um Tail zu vermeiden, 2.te Zeitachse anlegen\n",
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"coefficient_matrix = coefficient_matrix[:-coefficients]\n",
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"error_signal = (output - desired_signal)[:-coefficients]\n",
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"t2 = np.linspace(0, len(error_signal), len(error_signal))/10000\n",
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"\n",
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"# SNR davor/danach in dB berechnen, SNR Ratio berechnen, \n",
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"snr_before = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(noise_signal**2, t))\n",
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"snr_after = 10 * np.log10(np.trapz(desired_signal**2, t) / np.trapz(error_signal**2, t2))\n",
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"delta_snr = round(snr_after - snr_before, 2)\n",
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"\n",
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"if SIMULATION == True:\n",
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" # Soundfiles zu 16 Bit skalieren und als .txt speichern für DSP Simulation\n",
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" desired_signal_simulation = desired_signal*(2**(15)-1)\n",
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" noise_signal_simulation = noise_signal*(2**(15)-1)\n",
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" corrupted_signal_simulation = corrupted_signal*(2**(15)-1)\n",
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" np.savetxt('simulation_data/intermediate_desired_signal.txt', desired_signal_simulation, fmt='%d')\n",
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" np.savetxt('simulation_data/intermediate_corrupted_signal.txt', corrupted_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
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" np.savetxt('simulation_data/intermediate_noise_signal.txt', noise_signal_simulation, fmt='%d', delimiter=\"\\n\")\n",
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"\n",
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"# Plots des Filterprozesses\n",
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"figure1, (ax0, ax1, ax2, ax3) = plt.subplots(4, 1, figsize=(15, 12), sharex=True, sharey=True)\n",
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"ax0.set_ylim(-1, 1)\n",
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"ax0.plot(t, desired_signal, c='deepskyblue', label='Desired signal')\n",
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"ax1.plot(t, corrupted_signal, c='royalblue', label='Corrupted signal')\n",
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"ax2.plot(t, noise_signal, c='chocolate', label='Reference noise signal')\n",
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"ax3.plot(t, output, c='green', label=f'SNR Gain = {delta_snr} dB')\n",
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"\n",
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"ax0.text(0.5, -0.3, '(a) Desired signal',\n",
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" transform=ax0.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"ax1.text(0.5, -0.3, '(b) Corrupted signal',\n",
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" transform=ax1.transAxes,\n",
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" fontsize=25,\n",
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" fontweight='normal',\n",
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" ha='center',\n",
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" va='bottom')\n",
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"\n",
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"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 = 0.000\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))/20000\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))/20000\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_r11**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+scratching', '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))/20000\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_r11**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",
|
|
" dsp_desired_signal_r11 = desired_signal_r11*(2**(15)-1)\n",
|
|
" dsp_noise_signal_r11 = noise_signal_r11*(2**(15)-1)\n",
|
|
" dsp_noise_signal_vpu = noise_signal_vpu*(2**(15)-1)\n",
|
|
" dsp_corrupted_signal = dsp_desired_signal_r11 + dsp_noise_signal_r11\n",
|
|
" python_output = output*(2**(15)-1)\n",
|
|
" np.savetxt('simulation_data/complex_dsp_desired_signal_r11.txt', dsp_desired_signal_r11, fmt='%d')\n",
|
|
" np.savetxt('simulation_data/complex_dsp_noise_signal_r11.txt', dsp_noise_signal_r11, fmt='%d')\n",
|
|
" np.savetxt('simulation_data/complex_dsp_noise_signal_vpu.txt', dsp_noise_signal_vpu, fmt='%d', delimiter=\"\\n\")\n",
|
|
" np.savetxt('simulation_data/complex_dsp_corrupted_signal.txt', dsp_corrupted_signal, fmt='%d', delimiter=\"\\n\")\n",
|
|
" np.savetxt('filter_output/complex_python_output.txt', python_output, fmt='%d', delimiter=\"\\n\")\n",
|
|
" np.savetxt('filter_output/complex_python_filter_coefficients.txt', coefficient_matrix, fmt='%.4f', 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",
|
|
"# Alle Kurven in ein Array stapeln\n",
|
|
"all_curves = np.vstack([\n",
|
|
" male_breathing,\n",
|
|
" male_chewing,\n",
|
|
" male_scratching,\n",
|
|
" male_drinking,\n",
|
|
" male_coughing\n",
|
|
"])\n",
|
|
"\n",
|
|
"# Punktweiser Mittelwert\n",
|
|
"mean_curve = np.mean(all_curves, axis=0)\n",
|
|
"\n",
|
|
"# Plot\n",
|
|
"plt.figure(figsize=(15, 7))\n",
|
|
"plt.plot(x, male_breathing, linestyle='-', linewidth=1.5, alpha=0.7, label='Breathing Noise')\n",
|
|
"plt.plot(x, male_chewing, linestyle='--', linewidth=1.5, alpha=0.7, label='Chewing Noise')\n",
|
|
"plt.plot(x, male_scratching, linestyle='-.', linewidth=1.5, alpha=0.7, label='Scratching Noise')\n",
|
|
"plt.plot(x, male_drinking, linestyle=':', linewidth=1.5, alpha=0.7, label='Drinking Noise')\n",
|
|
"plt.plot(x, male_coughing, linestyle=(0, (3, 1, 1, 1)), linewidth=1.5, alpha=0.7, label='Coughing Noise')\n",
|
|
"plt.plot(x, mean_curve, linestyle='--', color='red', linewidth=2.5, label='Mean SNR-Gain')\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(\"Filter length\")\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='upper left')\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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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))/20000\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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Filterlänge und Update-Schritte Vergleich\n",
|
|
"\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"PLOT = True\n",
|
|
"\n",
|
|
"# Filterlänge\n",
|
|
"N = np.arange(1, 128)\n",
|
|
"# Verschiedene Updateschritte\n",
|
|
"U_values = [1, 0.5, 0.25]\n",
|
|
"\n",
|
|
"C_total_1 = N + (6*N + 8)*U_values[0] + 34\n",
|
|
"C_total_2 = N + (6*N + 8)*U_values[1] + 34\n",
|
|
"C_total_3 = N + (6*N + 8)*U_values[2] + 34\n",
|
|
"\n",
|
|
"plt.figure(figsize=(15, 7))\n",
|
|
"plt.plot(N, C_total_1, linestyle='-', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[0]}')\n",
|
|
"plt.plot(N, C_total_2, linestyle='--', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[1]}')\n",
|
|
"plt.plot(N, C_total_3, linestyle='-.', linewidth=1.5, alpha=0.7, label=f'1/U = {U_values[2]}')\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(\"Filter length\")\n",
|
|
"plt.ylabel(\"Cycles/Sample\")\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_c_total', dpi=600)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"C:\\Users\\phangl\\AppData\\Local\\Temp\\ipykernel_36048\\4147559445.py:86: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
|
|
" figure1.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 1500x900 with 3 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
|
|
"# Vergleich Output High/Low-Level\n",
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"\n",
|
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"PLOT = False\n",
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"\n",
|
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"python_output = np.loadtxt('filter_output/simple_python_output.txt', delimiter=\",\")/(2**(15)-1)\n",
|
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"dsp_output = np.loadtxt('filter_output/simple_dsp_output.txt', delimiter=\",\")[:-1]/(2**(15)-1)\n",
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"\n",
|
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"from scipy.stats import pearsonr\n",
|
|
"from scipy import signal\n",
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"\n",
|
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"# 1. Mean Squared Error (MSE)\n",
|
|
"mse = np.mean((python_output - dsp_output) ** 2)\n",
|
|
"# 2. Root Mean Squared Error (RMSE)\n",
|
|
"rmse = np.sqrt(mse)\n",
|
|
"# 3. Mean Absolute Error (MAE)\n",
|
|
"mae = np.mean(np.abs(python_output - dsp_output))\n",
|
|
"# 4. Pearson Correlation Coefficient (Lineare Korrelation)\n",
|
|
"correlation, p_value = pearsonr(python_output, dsp_output)\n",
|
|
"# 5. Normalized Mean Squared Error (NMSE) - normalisiert durch Varianz des Referenzsignals\n",
|
|
"nmse = np.sum((python_output - dsp_output) ** 2) / np.sum(dsp_output ** 2)\n",
|
|
"\n",
|
|
"diff = python_output - dsp_output\n",
|
|
"\n",
|
|
"t = np.linspace(0, 2000, 2000)/200\n",
|
|
"\n",
|
|
"figure1, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(15, 9), sharex=True, sharey=True)\n",
|
|
"ax1.set_ylim(1.0, -1.0)\n",
|
|
"ax1.plot(t, python_output, linestyle='-', c='deepskyblue', linewidth=1, alpha=1, label='High Level Simulation')\n",
|
|
"ax2.plot(t, dsp_output, linestyle='-', c='indianred', linewidth=1, alpha=1, label='Low Level Simulation')\n",
|
|
"ax3.plot(t, python_output, linestyle='-', c='deepskyblue', linewidth=1, alpha=1, label='High Level Simulation')\n",
|
|
"ax3.plot(t, dsp_output, linestyle='-', c='indianred', linewidth=1, alpha=0.7, label=f'Low Level Simulation (Pearson Corr: {correlation:.3f})')\n",
|
|
"ax3.plot(t, diff, linestyle='-', c='green', linewidth=1, alpha=0.7, label=f'Difference')\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",
|
|
"ax1.text(0.5, -0.3, '(a) High Level Simulation',\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) Low Level Simulation',\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'(c) Comparision High/Low Level Simulation (Pearson Corr: {correlation:.3f})',\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",
|
|
"ax1.set_ylabel(\"Amplitude\")\n",
|
|
"ax2.set_ylabel(\"Amplitude\")\n",
|
|
"ax3.set_ylabel(\"Amplitude\")\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",
|
|
"\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",
|
|
"ax1.spines['right'].set_visible(False)\n",
|
|
"ax2.spines['right'].set_visible(False)\n",
|
|
"ax3.spines['right'].set_visible(False)\n",
|
|
"\n",
|
|
"\n",
|
|
"figure1.tight_layout()\n",
|
|
"if PLOT == True:\n",
|
|
" plt.savefig(f'plots/fig_high_low_comparison', dpi=600)\n",
|
|
"figure1.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Reduced Update Plot\n",
|
|
"\n",
|
|
"import matplotlib.ticker as mtick\n",
|
|
"\n",
|
|
"PLOT = True\n",
|
|
"\n",
|
|
"# Daten aus .csv laden\n",
|
|
"data_reduced_update = np.loadtxt('snr_evaluation/reduced_update', delimiter=\",\")\n",
|
|
"\n",
|
|
"# Daten laden\n",
|
|
"x = data_reduced_update[:, 0]\n",
|
|
"reduced_update = savgol_filter(data_reduced_update[:, 2], 3, 2)\n",
|
|
"\n",
|
|
"# Plot\n",
|
|
"plt.figure(figsize=(15, 7))\n",
|
|
"plt.plot(x, reduced_update, linestyle='--', color='indianred', linewidth=2, alpha=0.9, label='SNR-Gain (Male Voice + Breathing 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",
|
|
"\n",
|
|
"plt.xlabel(\"Update Rate\")\n",
|
|
"plt.ylabel(\"Relative SNR-Gain\")\n",
|
|
"plt.grid(True, linestyle='-.', alpha=0.4)\n",
|
|
"#Spines auf ganzen Plot anwenden\n",
|
|
"plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
|
|
"plt.gca().spines['top'].set_visible(False)\n",
|
|
"plt.gca().spines['right'].set_visible(False)\n",
|
|
"plt.gca().invert_xaxis()\n",
|
|
"plt.legend(frameon=False, loc='upper right')\n",
|
|
"plt.tight_layout()\n",
|
|
"if PLOT == True:\n",
|
|
" plt.savefig(f'plots/fig_snr_reduced_update', dpi=600)\n",
|
|
"plt.show()\n"
|
|
]
|
|
}
|
|
],
|
|
"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,
|
|
"nbformat_minor": 2
|
|
}
|