149 lines
3.1 KiB
Python
149 lines
3.1 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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n_plots = 7
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endtime = 4
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size = 1000
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time = np.linspace(0,endtime,size)
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audio = 1023*np.random.random(size=(size))
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audio = 512+(audio-512)*(0.2)*np.sin(np.linspace(0,endtime*2*3.14*2, size))+(audio-512)*0.3
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#fig, axs = plt.subplots(n_plots, 1, sharex=True)
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fig = plt.figure(figsize=(10,6))
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gs = fig.add_gridspec(n_plots, hspace=0)
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axs = gs.subplots(sharex=True)
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axs[0].set_xlim((-0,4))
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titlex = 0.5
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titley = 0.5
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axs[0].plot(time,audio)
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axs[0].set_ylabel("Mikrofon\n-signal", x=titlex, y=titley)
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audio_norm = audio-512
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# axs[1].plot(time,audio_norm)
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# axs[1].set_ylabel("Mikrofon\n-signal\nnormiert", x=titlex, y=titley)
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offset = 1
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spc = size/endtime
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audio_norm = np.array(audio_norm)
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audio_squared = np.square(audio_norm)
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axs[2-offset].plot(time, audio_squared)
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axs[2-offset].set_ylabel("Signal\n-energie\n(gefiltert)", x=titlex, y=titley)
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spc = int(size/endtime/40)
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chunks = list()
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chunktimes = list()
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energy = 0
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i = 0
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for sample, timepoint in zip(audio_squared, time):
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energy += sample
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i += 1
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if i > spc:
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i = 0
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chunks.append(energy)
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chunktimes.append(timepoint)
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energy = 0
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axs[3-offset].plot(chunktimes, chunks)
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axs[3-offset].set_ylabel("Signale\n-nergie\nchunks", x=titlex, y=titley)
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n_BP = 5
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SAMPLING_FREQUENCY_BP = 40
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import math
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filter_outputs = list()
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angles = list()
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angles2 = list()
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delayed = list()
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for i in range(n_BP):
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filter_output = list()
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Q = 10
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frequency = 2.2 + i * 0.2
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w0 = 2. * 3.1415 * frequency / SAMPLING_FREQUENCY_BP
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a = math.sin(w0 / (2. * Q))
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b0 = a
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b1 = 0.
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b2 = -a
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a0 = 1. + a
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a1 = -2. * math.cos(w0)
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a2 = 1. - a
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x, xn1, xn2, yn1, yn2 = 0,0,0,0,0
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yn3, yn4, yn5 = 0,0,0
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angle2 = 0
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for chunk in chunks:
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y = b0 * chunk + b1 * xn1 + b2 * xn2 - yn1 * a1 - yn2 * a2
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xn2 = xn1
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xn1 = x
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yn5 = yn4
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yn4 = yn3
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yn3 = yn2
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yn2 = yn1
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yn1 = y
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if i==1:
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angle = math.atan2(yn5, y)
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angles.append(angle)
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delayed.append(yn5)
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PI = 3.141592
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if (PI < abs(angle - angle2) and abs(angle - angle2) < 3 * PI):
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angle2 = angle + 2 * PI
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else:
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angle2 = angle
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angles2.append(angle2)
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filter_output.append(y)
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axs[4-offset].plot(chunktimes, filter_output, color="k" if i == 1 else "#1f77b4")
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if i == 1:
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axs[5-offset].plot(chunktimes, filter_output)
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filter_outputs.append(filter_output)
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axs[4-offset].set_ylabel("Band\n-pässe", x=titlex, y=titley)
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axs[5-offset].plot(chunktimes, delayed, color="k")
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axs[5-offset].set_ylabel("Delay", x=titlex, y=titley)
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axs[6-offset].plot(chunktimes, angles)
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axs[6-offset].set_ylabel("Geschätzter\nPhasen\nwinkel", x=titlex, y=titley)
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axs[7-offset].plot(chunktimes, angles2)
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axs[7-offset].set_ylabel("Halbierte\nFrequenz", x=titlex, y=titley)
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for i in range(n_plots):
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axs[i].set_yticks(())
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plt.savefig("beaterkennung.svg")
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plt.savefig("beaterkennung.png", dpi=500)
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plt.show() |