import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import find_peaks

x = np.sin(2*np.pi*(2**np.linspace(2,10,1000))*np.arange(1000)/48000) + np.random.normal(0, 1, 1000) * 0.15
peaks, _ = find_peaks(x, distance=20)
peaks2, _ = find_peaks(x, prominence=1)      # BEST!
peaks3, _ = find_peaks(x, width=20)
peaks4, _ = find_peaks(x, threshold=0.4)     # Required vertical distance to its direct neighbouring samples, pretty useless
plt.subplot(2, 2, 1)
plt.plot(peaks, x[peaks],"xr"); plt.plot(x); plt.legend(['distance'])
plt.subplot(2, 2, 2)
plt.plot(peaks2, x[peaks2],"ob"); plt.plot(x); plt.legend(['prominence'])
plt.subplot(2, 2, 3)
plt.plot(peaks3, x[peaks3],"vg"); plt.plot(x); plt.legend(['width'])
plt.subplot(2, 2, 4)
plt.plot(peaks4, x[peaks4],"xk"); plt.plot(x); plt.legend(['threshold'])
plt.show()

参考：

  1. python/scipy的寻峰算法