Matlab Sound Event Detection The purpose of this tutorial is to explore the use of Matlab’s fminsearchbnd function to perform sound event detection. The function is used to find a minimum bounding box around a sound event. This process can be used to detect events such as car honking, or footsteps. The function also finds the centroid and amplitude of the sound event.

This example was written by Mike Pugh in his book “Matlab Programming for Signal Processing”, 2nd Edition (ISBN-10: 0123881387). It is available at www.peachpit.com/store/matlab-programming-for-signal-processing-2e

To run this example, you will need to download the following files from the website:

sound_event_detection_code.m

sound_event_detection_data.mat

Matlab Sound Event Detection

In this example, we will be using the MATLAB to detect sound events. We will also be demonstrating the use of the MATLAB function ‘findpeaks’ to analyze the sound signal for peaks.

The main purpose of this example is to demonstrate how we can use MATLAB for detecting sound events in real-time. We will also show how to use the findpeaks function in MATLAB to detect peaks in a sound signal.

Matlab Sound Event Detection

Matlab allows you to analyze audio signals and extract features from them, such as pitch, loudness, duration, etc. It also has a function called waveform which can be used to create spectrograms of sound files.

In this tutorial, we will show you how to use Matlab’s waveform function to create a spectrogram of an audio file. We will then use the resulting image data as input for Matlab’s sound event detection function. This code can be found in our GitHub Repository: https://github.com/numericalmethods/sound-event-detection

Matlab Sound Event Detection

In this post, we are going to see how to detect sound event in matlab using wavelets. If you want to know what sound event is, please check this link .

We will be using wav files for demonstration purpose, but it can be extended to any other kind of data. The main idea is that we will be breaking up the signal into smaller segments and then applying wavelet transform (WT) on them. After applying WT on each segment, we can find out the energy value of each frequency bin at different time instants. If the energy value is high enough at a particular time instant then it means that there was a significant increase/decrease in sound intensity at that time instant and hence we can say there has been a sound event at that point in time. The figure below shows how WT works on a signal:

We will be using this Matlab function by Dr. Lihong Li which performs WT on audio signals very efficiently: Link . Here is another link which provides good theoretical background on WT: Link .