Homemade Bird Buddy

Introduction

This is a Python script designed for real-time bird audio detection and analysis. It uses audio recording, species prediction, and data storage to create an automated system for identifying and logging bird species in a specific area.

Github

Key Components and Functionality

1. Imports and Initial Setup

The script begins by importing necessary libraries and modules, including:

• Standard libraries: datetime, os, pathlib, random, time
• Third-party libraries: requests, elasticsearch, birdnet, sounddevice, soundfile
• Custom modules: Bird, Response (from bird.py), and configuration settings (from config.py)

2. Elasticsearch Configuration

The script sets up a connection to Elasticsearch for data storage:

es = elasticsearch.Elasticsearch(elasticsearch_host, ca_certs=cert_loc, http_auth=(elasticsearch_user, elasticsearch_password), max_retries=0, retry_on_timeout=False)

Putting this into Elasticsearch allows me to keep track of birds over time, as well as any other metadata analysis I may want to perform

3. Audio Recording Setup

It configures audio recording parameters:

• Sample rate: 48000 Hz
• Recording duration: 3 seconds
• Input device: 'Logi C615 HD WebCam, MME', stuck right outside my window

4. Species Prediction

The script uses BirdNET to predict species in the area:
species_in_area = birdnet.predict_species_at_location_and_time(35.055851, -80.684868)
This uses TensorFlow to compare against existing models with your recorded audio. I found this to be quite accurate and fast. Since this was also setup to copy out matching audio, I was able to go behind it and double check if I could really hear it.

5. Discord Notification Function

This was more optional on my end to get real-time notifications. A simple request function send_discord_notification is defined to send bird information to a Discord webhook when a high-confidence prediction is made.

Discord Webhook Integration

6. Main Loop

The core functionality is in a continuous loop that performs the following steps:

1. Record audio
2. Predict bird species from the audio
3. Fetch detailed information about predicted species from an API
4. Store the information in Elasticsearch
5. Send a Discord notification for high-confidence predictions
6. Archive audio files for significant detections

Birds showing up over time with a confidence > 0.1

Key Features

• Real-time audio recording and analysis
• Integration with BirdNET for species prediction
• Data enrichment using an external API
• Elasticsearch storage for long-term data analysis
• Discord notifications for significant detections
• Audio archiving for high-confidence predictions

What's Next?

• Offload audio prediction into a separate script so that we can continuously record and analyze in 3 second increments
• Setup the webcam outside of our window feeder instead to take videos or pictures on movement or prediction match
• Re-train models using output files for even more accurate results