Building a Portable Soundscape Monitoring App

In this tutorial, we will explore how to create a portable soundscape monitoring app using AudioMoth, Raspberry Pi, and a touch of deep learning. This project is perfect for beginners who are interested in sound monitoring and machine learning.

Prerequisites

Before we dive into the project, make sure you have the following:

• A basic understanding of programming concepts.
• Familiarity with Python, as we will be using it for our deep learning models.
• A Raspberry Pi (any model should work, but Raspberry Pi 3 or later is recommended).
• An AudioMoth device for sound recording.
• Access to a computer with internet connectivity for downloading necessary software.

Step-by-Step Guide

Step 1: Setting Up Your Raspberry Pi

First, you need to set up your Raspberry Pi. Follow these steps:

1. Download the latest version of Raspberry Pi OS from the official website.
2. Use a tool like Balena Etcher to flash the OS onto a microSD card.
3. Insert the microSD card into your Raspberry Pi and power it on.
4. Connect your Raspberry Pi to a monitor, keyboard, and mouse to complete the initial setup.

Step 2: Installing Required Software

Once your Raspberry Pi is set up, you need to install some software:

1. Open a terminal window on your Raspberry Pi.
2. Update your package list by running the command: sudo apt update.
3. Install Python and pip (Python package manager) with the command: sudo apt install python3 python3-pip.
4. Install necessary libraries for deep learning, such as TensorFlow or PyTorch, using pip. For example: pip3 install tensorflow.

Step 3: Configuring AudioMoth

Next, you need to configure your AudioMoth device:

1. Download the AudioMoth configuration software from the official website.
2. Connect your AudioMoth to your computer via USB.
3. Use the configuration software to set the recording parameters, such as sample rate and duration.
4. Save the configuration and disconnect the AudioMoth.

Step 4: Recording Soundscapes

Now that your AudioMoth is configured, you can start recording soundscapes:

1. Place the AudioMoth in your desired location.
2. Turn on the device to start recording.
3. After the recording session, retrieve the AudioMoth and transfer the audio files to your Raspberry Pi.

Step 5: Analyzing Audio Data with Deep Learning

With your audio data in hand, it’s time to analyze it using deep learning:

1. Load your audio files into Python using libraries like librosa for audio processing.
2. Preprocess the audio data to extract features that can be used for analysis.
3. Train a deep learning model on your audio features to classify or analyze the soundscapes.

Explanation of Key Concepts

Throughout this tutorial, we have touched on several key concepts:

• AudioMoth: A low-cost, open-source audio recorder designed for environmental monitoring.
• Raspberry Pi: A small, affordable computer that can be used for various projects, including IoT applications.
• Deep Learning: A subset of machine learning that uses neural networks to analyze data and make predictions.

Conclusion

Congratulations! You have successfully built a portable soundscape monitoring app using AudioMoth and Raspberry Pi. This project not only enhances your technical skills but also contributes to understanding environmental soundscapes. Feel free to explore further by experimenting with different deep learning models or expanding your monitoring capabilities.

The post Audio Spectrogram Transformers Beyond the Lab appeared first on Towards Data Science.