47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
import numpy as np
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#import sounddevice as sd
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import matplotlib.pyplot as plt
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def normalize_signal(signal):
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min_amp = np.min(signal)
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normalized_signal = signal - min_amp
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max_amp = np.max(normalized_signal)
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normalized_signal *= 2.0/max_amp
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normalized_signal -= 1
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return normalized_signal
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f = 466.16
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f_s = 16000
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T_0 = 1/f
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t = np.arange(0,T_0,1/f_s)
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s = 0.5 * np.sin(2*np.pi*f*t)
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# Convert signal covariance
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var_s = np.cov(s)
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# Calculate required noise variance
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var_snr_10 = var_s/(10**(10/10))
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var_snr_20 = var_s/(10**(20/10))
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var_snr_30 = var_s/(10**(30/10))
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# Genearate noise
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noise_snr_10 = (var_snr_10**0.5) * np.random.randn(len(s))
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noise_snr_20 = (var_snr_20**0.5) * np.random.randn(len(s))
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noise_snr_30 = (var_snr_30**0.5) * np.random.randn(len(s))
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# Add signal and noise
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m_snr_10 = s+noise_snr_10
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m_snr_20 = s+noise_snr_20
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m_snr_30 = s+noise_snr_30
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plt.plot(t,s, label="Pure A$\sharp$ Tone")
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plt.plot(t,m_snr_10, label="Corrupted A$\sharp$ Tone (SNR=10dB)")
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plt.plot(t,m_snr_20, label="Corrupted A$\sharp$ Tone (SNR=20dB)")
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plt.plot(t,m_snr_30, label="Corrupted A$\sharp$ Tone (SNR=30dB)")
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plt.legend()
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plt.title("Pure and Corrupted A$\sharp$ Tones")
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plt.xlabel("Time (s)")
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plt.show()
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#sd.play(normalize_signal(s), samplerate=f_s, blocking=True)
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