我如何修改Python脚本来阅读ham无线电频率？

我发现了一篇关于如何使用Python和RTLSDR库从RTLSDR获取广播调频广播的精彩文章，这篇文章可能太详细了。

经过一些修改，我可以直接播放广播音频片段：

#from pylab import psd, xlabel, ylabel, show
import matplotlib.pyplot as plt
from rtlsdr import RtlSdr
from scipy import signal
from time import sleep
import numpy as np
from scipy.io import wavfile
import sounddevice

sdr = RtlSdr()

# configure device
Freq = 91.7e6 #mhz
Fs = 1140000
F_offset = 250000
Fc = Freq - F_offset
sdr.sample_rate = Fs
sdr.center_freq = Fc
sdr.gain = 'auto'
samples = sdr.read_samples(8192000)
print(samples)
print(samples.shape)
print(np.max(samples))
#continue
#sdr.close()
x1 = np.array(samples).astype("complex64")
fc1 = np.exp(-1.0j*2.0*np.pi* F_offset/Fs*np.arange(len(x1))) 
x2 = x1 * fc1
# plt.specgram(x2, NFFT=2048, Fs=Fs)  
# plt.title("x2")  
# plt.xlabel("Time (s)")  
# plt.ylabel("Frequency (Hz)")  
# plt.ylim(-Fs/2, Fs/2)  
# plt.xlim(0,len(x2)/Fs)  
# plt.ticklabel_format(style='plain', axis='y' )  
# plt.show()
bandwidth = 200000#khz broadcast radio.
n_taps = 64
# Use Remez algorithm to design filter coefficients
lpf = signal.remez(n_taps, [0, bandwidth, bandwidth+(Fs/2-bandwidth)/4, Fs/2], [1,0], Hz=Fs)  
x3 = signal.lfilter(lpf, 1.0, x2)
dec_rate = int(Fs / bandwidth)
x4 = x3[0::dec_rate]
Fs_y = Fs/dec_rate
f_bw = 200000  
dec_rate = int(Fs / f_bw)  
x4 = signal.decimate(x2, dec_rate) 
# Calculate the new sampling rate
# Fs_y = Fs/dec_rate  
# plt.scatter(np.real(x4[0:50000]), np.imag(x4[0:50000]), color="red", alpha=0.05)  
# plt.title("x4")  
# plt.xlabel("Real")  
# plt.xlim(-1.1,1.1)  
# plt.ylabel("Imag")  
# plt.ylim(-1.1,1.1)   
# plt.show() 
y5 = x4[1:] * np.conj(x4[:-1])
x5 = np.angle(y5)
# plt.psd(x5, NFFT=2048, Fs=Fs_y, color="blue")  
# plt.title("x5")  
# plt.axvspan(0,             15000,         color="red", alpha=0.2)  
# plt.axvspan(19000-500,     19000+500,     color="green", alpha=0.4)  
# plt.axvspan(19000*2-15000, 19000*2+15000, color="orange", alpha=0.2)  
# plt.axvspan(19000*3-1500,  19000*3+1500,  color="blue", alpha=0.2)  
# plt.ticklabel_format(style='plain', axis='y' )  
# plt.show()


# The de-emphasis filter
# Given a signal 'x5' (in a numpy array) with sampling rate Fs_y
d = Fs_y * 75e-6   # Calculate the # of samples to hit the -3dB point  
x = np.exp(-1/d)   # Calculate the decay between each sample  
b = [1-x]          # Create the filter coefficients  
a = [1,-x]  
x6 = signal.lfilter(b,a,x5)  
audio_freq = 48100.0  
dec_audio = int(Fs_y/audio_freq)  
Fs_audio = Fs_y / dec_audio
x7 = signal.decimate(x6, dec_audio) 

sounddevice.play(x7,Fs_audio) 
x7 *= 10000 / np.max(np.abs(x7))  

#sounddevice.play(x7,Fs_audio)
x7.astype("int16").tofile("wbfm-mono.raw")  #Raw file.
wavfile.write('wav.wav',int(Fs_audio), x7.astype("int16"))
print('playing...')
sounddevice.play(x7.astype("int16"),Fs_audio,blocking=True)
#print('try2')
#Sounds no good
#sounddevice.play(x7, blocking=True)
# use matplotlib to estimate and plot the PSD
#psd(samples, NFFT=1024, Fs=sdr.sample_rate/1e6, Fc=sdr.center_freq/1e6)
#xlabel('Frequency (MHz)')
#ylabel('Relative power (dB)')
#print("showing")
#show()

现在，上面的工作原理与他们设计的一样，但当我做了一些更改，使其读取火腿无线电中继器（5khz宽度而不是200khz），我让它工作起来，但有几个问题：

音频比GQRX中的音频有一点高音静态

没有静噪-不需要恒定的广播调频，我如何处理静噪，使其在没有人说话时不会有响亮的静电

有没有什么步骤或处理我忘记改变这里试图解码业余无线电频率

#from pylab import psd, xlabel, ylabel, show
import matplotlib.pyplot as plt
from rtlsdr import RtlSdr
from scipy import signal
from time import sleep
import numpy as np
from scipy.io import wavfile
import sounddevice

sdr = RtlSdr()

# configure device
Freq = 440.712e6 #mhz
Fs = 1140000
F_offset = 2500
Fc = Freq - F_offset
sdr.sample_rate = Fs
sdr.center_freq = Fc
sdr.gain = 'auto'
samples = sdr.read_samples(8192000)
print(samples)
print(samples.shape)
print(np.max(samples))
#continue
#sdr.close()
x1 = np.array(samples).astype("complex64")
fc1 = np.exp(-1.0j*2.0*np.pi* F_offset/Fs*np.arange(len(x1))) 
x2 = x1 * fc1
plt.specgram(x2, NFFT=2048, Fs=Fs)  
plt.title("x2")  
plt.xlabel("Time (s)")  
plt.ylabel("Frequency (Hz)")  
plt.ylim(-Fs/2, Fs/2)  
plt.xlim(0,len(x2)/Fs)  
plt.ticklabel_format(style='plain', axis='y' )  
plt.show()
bandwidth = 2500#khz broadcast radio.
n_taps = 64
# Use Remez algorithm to design filter coefficients
lpf = signal.remez(n_taps, [0, bandwidth, bandwidth+(Fs/2-bandwidth)/4, Fs/2], [1,0], Hz=Fs)  
x3 = signal.lfilter(lpf, 1.0, x2)
dec_rate = int(Fs / bandwidth)
x4 = x3[0::dec_rate]
Fs_y = Fs/dec_rate
f_bw = 200000  
dec_rate = int(Fs / f_bw)  
x4 = signal.decimate(x2, dec_rate) 
# Calculate the new sampling rate
Fs_y = Fs/dec_rate  
# plt.scatter(np.real(x4[0:50000]), np.imag(x4[0:50000]), color="red", alpha=0.05)  
# plt.title("x4")  
# plt.xlabel("Real")  
# plt.xlim(-1.1,1.1)  
# plt.ylabel("Imag")  
# plt.ylim(-1.1,1.1)   
# plt.show() 
y5 = x4[1:] * np.conj(x4[:-1])
x5 = np.angle(y5)
# plt.psd(x5, NFFT=2048, Fs=Fs_y, color="blue")  
# plt.title("x5")  
# plt.axvspan(0,             15000,         color="red", alpha=0.2)  
# plt.axvspan(19000-500,     19000+500,     color="green", alpha=0.4)  
# plt.axvspan(19000*2-15000, 19000*2+15000, color="orange", alpha=0.2)  
# plt.axvspan(19000*3-1500,  19000*3+1500,  color="blue", alpha=0.2)  
# plt.ticklabel_format(style='plain', axis='y' )  
# plt.show()


# The de-emphasis filter
# Given a signal 'x5' (in a numpy array) with sampling rate Fs_y
d = Fs_y * 75e-6   # Calculate the # of samples to hit the -3dB point  
x = np.exp(-1/d)   # Calculate the decay between each sample  
b = [1-x]          # Create the filter coefficients  
a = [1,-x]  
x6 = signal.lfilter(b,a,x5)  
audio_freq = 41000.0  
dec_audio = int(Fs_y/audio_freq)  
Fs_audio = Fs_y / dec_audio
x7 = signal.decimate(x6, dec_audio) 

sounddevice.play(x7,Fs_audio) 
x7 *= 10000 / np.max(np.abs(x7))  

#sounddevice.play(x7,Fs_audio)
x7.astype("int16").tofile("wbfm-mono.raw")  #Raw file.
wavfile.write('wav.wav',int(Fs_audio), x7.astype("int16"))
print('playing...')
sounddevice.play(x7.astype("int16"),Fs_audio,blocking=True)
#print('try2')
#Sounds no good
#sounddevice.play(x7, blocking=True)
# use matplotlib to estimate and plot the PSD
#psd(samples, NFFT=1024, Fs=sdr.sample_rate/1e6, Fc=sdr.center_freq/1e6)
#xlabel('Frequency (MHz)')
#ylabel('Relative power (dB)')
#print("showing")
#show()