Sound Classification On Raspberry Pi With Tensorflow

Abstract

1. Introduction

The project is developed and tested with Python 2.7.

Set the right path where you downloaded the dataset in your code.

Set the right path where you want to save the trained model.

2. Objective

In this project is presented a simple method to train an MLP neural network for audio signals. The trained model can be exported on a Raspberry Pi (2 or superior suggested) to classify audio signal registered with USB microphone

This project demonstrates how Python, Machine Learning, TensorFlow can be applied to a real-world Internet of Things (IoT) problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/GianlucaPaolocci/Sound-classification-on-Raspberry-Pi-with-Tensorflow.git
cd Sound-classification-on-Raspberry-Pi-with-Tensorflow

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add a mobile dashboard using Blynk or Firebase
• Store readings in a cloud database for history charts
• Add alerts via SMS / Telegram when thresholds are crossed

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which microcontroller / board and sensors are used and why?
2. How does the device send data (Wi-Fi, MQTT, HTTP, Bluetooth)?
3. Where is the sensor data stored and visualised?
4. How is power consumption managed?
5. How would you secure the device and its communication?

9. Source Code & License

This project is developed by GianlucaPaolocci and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.