给定使用以下代码计算的频谱图：
import matplotlib.pyplot as plt
import numpy as np
import scipy
from scipy import signal, fft
from scipy.io import wavfile
from skimage import util
import librosa
import pylab
# from scipy.fftpack import fft
def stft(x, fs, framesz, hop):
framesamp = int(framesz*fs)
hopsamp = int(hop*fs)
w = scipy.hanning(framesamp)
X = scipy.array([scipy.fft(w*x[i:i+framesamp]) for i in range(0, len(x)-framesamp, hopsamp)])
return X
def istft(X, fs, T, hop):
x = scipy.zeros(T*fs)
framesamp = X.shape[1]
hopsamp = int(hop*fs)
for n,i in enumerate(range(0, len(x)-framesamp, hopsamp)): x[i:i+framesamp] += scipy.real(scipy.ifft(X[n]))
return x
audio_data = f'../recordings/SDRuno_20220309_140138Z_1kHz.wav' # y movement as test...
fs, data = wavfile.read(audio_data)
data = np.mean(data, axis=1) # convert to mono by avg l+r channels
N = data.shape[0]
L = N / fs
amp = 2 / N * np.abs(data)
print(f'Audio length: {L:.2f} seconds')
# f, ax = plt.subplots()
# ax.plot(np.arange(N) / fs, data)
# ax.set_xlabel('Time [s]')
# ax.set_ylabel('Amplitude [unknown]');
# plt.show()
M = int(fs * 0.001 * 20) # originally we had 1024, however now we use this for 20ms window resolution
# amp = 2 * np.sqrt(2)
stft_sig_f, stft_sig_t, stft_sig_Zxx = signal.stft(data, fs=fs, window='hann', nperseg=M, detrend=False)
# plt.pcolormesh(stft_sig_t, stft_sig_f, np.abs(stft_sig_Zxx), vmin=0, vmax=amp, shading='gouraud')
# plt.title('STFT Magnitude')
# plt.ylabel('Frequency [Hz]')
# plt.xlabel('Time [sec]')
# plt.show()
freqs, times, Sx = signal.spectrogram(data, fs=fs, window='hanning', nperseg=M, detrend=False, scaling='spectrum')
f, ax = plt.subplots(figsize=(10,5))
ax.pcolormesh(times, freqs / 1000, 10 * np.log10(Sx), cmap='inferno')
ax.set_ylabel('Frequency [kHz]')
ax.set_xlabel('Time [s]');
plt.show()
如何使用频谱图的“更清晰的信号”来过滤音频。我确实在某处读到过，这Sx实际上Zxx**2是Zxx可以用于 ISTFT 来重建音频的，但是我的知识非常缺乏。任何帮助，将不胜感激！
编辑：绘制 STFT 的当前代码只显示一个黑色窗口。
此示例中使用的 IQ WAV 文件可在此处找到：https ://www.dropbox.com/s/at31myl5oufvfa3/SDRuno_20220309_140138Z_1kHz.wav?dl=0