Building Siren Detection machine learning model

This tutorial covers end-to-end information on how to build, train, evaluate and deploy a siren detection machine learning model using Imagimob Studio and Infineon PSoC™6 development kit.


We will build a siren detection machine learning model that recognize the siren sounds from the emergency vehicles. This can be used to alert pedestrians to emergency vehicles in their vicinity, or to trigger other safety features in wearables.


Before starting this tutorial, you must meet the following:

After setting up everything, you can start building the siren detection machine learning model following the step-by-step instructions from the tutorial. The tutorial covers end-to-end machine learning stages from the data collection to deploying the generated model.

Let's start building the model

We will cover the following machine learning development flow:

  • Data Preparation: The first step is to collect and label data from sensors using Serial Capture in Graph UX and Infineon PSoC™ 6 development kit. After collecting and labeling data, we will import data into the Siren Starter project followed by distributing the data into the different datasets.

  • Preprocessing: The second main step is preprocessing where the data signal processing techniques are applied to the sensor data, such as windowing and filtering techniques to reduce the noise and extract the features of the sensors data.

  • Model Training: After building the preprocessor, data is passed to the model for training. In this step, we will generate, train and evaluate the model to find the best fitting siren detection model.

  • Model Deployment: The last step is to deploy the model, we will take the trained model and generate the source code in Studio and run the model on the development kit using ModusToolbox™ and ModusToolbox™ Machine Learning solution.