“**Signal Processing**” involves manipulating and analyzing signals to extract valuable information or enhance the quality of the signals. It consists of a wide range of techniques, including filtering, noise reduction, spectral analysis, time-frequency analysis, and more. In Python, the “**scipy**” library provides a dedicated signal processing module named “**scipy.signal**”.

This article provides a thorough guide on Python “**Scipy Signal Processing**” using numerous examples.

## How to Use the Python SciPy Signal Processing?

The “scipy” signal processing library is a collection of Python modules that provide a wide range of signal processing tools for analyzing the signal, detecting and removing noise from the signal, calculating the convolution of the signal, etc. The “**scipy.signal**” module is imported from the “**scipy**” library and used with a variety of signal processing functions to perform various signal processing operations.

Let’s understand it via the following examples:

**Example 1: Convolution of Two Arrays**

Convolution is a fundamental operation in signal processing that combines two functions to produce a third function representing the modification of one function by the other. To perform convolution in “SciPy”, the “**convolve()**” function from the signal processing module is used in the below code:

from scipy import signal

x = numpy.array([1, 2, 3, 4, 5])

h = numpy.array([0.5, 0.25, 0.125])

y = signal.convolve(x, h)

print(y)

In the above code snippet:

- The “
**numpy**” library and the “**signal**” module (from the “**scipy**” library) are imported, respectively. - The “
**array()**” function is used to create the arrays “**x**” and “**h**”. - The “
**convolve()**” function takes the “x” and “h” arrays as arguments and performs a discrete linear convolution of two one-dimensional arrays.

**Output**

The convolution of two given arrays has been displayed.

**Example 2: Finding Peaks in a Signal**

The “**scipy.signal**” module also provides functions for finding peaks in a signal. Here’s an example using the “**signal.find_peaks()**” function:

from scipy import signal

import matplotlib.pyplot as plt

# Generate a simple signal with peaks

t = numpy.linspace(0, 1, 1000)

x = numpy.sin(4 * numpy.pi * t) + numpy.sin(10 * numpy.pi * t)

# Find peaks in the signal

peaks, _ = signal.find_peaks(x)

# Plot the signal and peaks

plt.plot(t, x)

plt.plot(t[peaks], x[peaks], 'ro')

plt.xlabel('Time')

plt.ylabel('Amplitude')

plt.show()

According to the above code:

- The signal with two sine waves of different frequencies is generated using the “
**sin()**” function. - The “
**find_peaks()**” function is used to find the peaks in the signal. - Finally, the red circles are drawn around the detected peaks in the signal plot using the “
**plot()**” function.

**Output**

In the above output, the peak points in the signal have been shown.

**Example 3: Cross-correlation of Signals**

This example uses the scipy “**signal.correlate()**” function to compute the cross-correlation between two signals:

from scipy import signal

# Generate two example signals

x = numpy.array([1, 2, 3, 4, 5])

y = numpy.array([5, 4, 3, 2, 1])

# Compute the cross-correlation

correlation = signal.correlate(x, y, mode='same')

print(correlation)

In the above code lines:

- The two signals “x” and “y” are created using the “
**array()**” function. - After that, the “
**correlate()**” function is utilized to determine the cross-correlation between the two particular signals.

**Output**

The cross-correlation between the given signals has been calculated accordingly.

## Conclusion

The “scipy.signal” module provides various functions that are used to perform the convolution of two arrays, find peaks in a signal, apply cross-correlation of signals, etc. This article explored the capabilities and uses of SciPy’s signal processing in Python.