SciPy Spectrogram

by Online Tutorials Library July 14, 2022

SciPy Spectrogram

The signal processing toolbox consists of few filtering functions, a limited set of filter design tools. It also contains few B-spline interpolation algorithm for one-and two-dimensional data.

The scipy.signal.spectrogram computes a spectrogram with the consecutive Fourier transforms. Spectrograms can be used as a way of reflecting the change of frequency content of non-signal signals over time.

  from scipy import signal  import matplotlib.pyplot as plt  import numpy as np  #Generate a test signal, a 2 Vrms sine wave whose frequency linearly changes with time from 1kHz to 2kHz, corrupted by 0.001 V**2/Hz of white noise sampled at 10 kHz.  fs = 10e3 # Sampling Frequency  N = 1e5  amp = 2 * np.sqrt(2)  noise_power = 0.001 * fs / 2  time = np.arange(N) / fs  freq = np.linspace(1e3, 2e3, N)  x = amp * np.sin(2*np.pi*freq*time)  x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)  # Compute and plot the spectrogram.  f, t, Sxx = signal.spectrogram(x, fs)  plt.pcolormesh(t, f, Sxx)  plt.ylabel(‘Frequency [Hz]’)  plt.xlabel(‘Time [sec]’)  plt.show()  

Output:

The scipy.signal.spectogram() returns the following array:

f: ndarray

Array of sample frequencies.

t: ndarray

Array of segment times.

Sxx: ndarry

Spectogram of x. By default the last axis of Sxx corresponds to the segment times

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