A tongue-in-cheek demonstration of MongoDB's Vector Search capabilities for anomaly detection through sound input, using a basic handheld fan as our makeshift wind turbine. This demo enables real-time diagnosis by analyzing the emitted audio, allowing us to diagnose its condition—whether it's operating normally, stopped, or experiencing any issues.

Install Node.js (Tested with Node.js v20.8.0)

Create a file called .env in the main directory alongside the add_audio.py file and add your atlas connection string, in the following format:

MONGO_CONNECTION_STRING="mongodb+srv://connectionstringfromatlas"

Then copy this file in to the nodeUI directory too.

Install the required python modules.

pip install pyaudio

pip install numpy

pip install pymongo

pip install librosa

pip install panns_inference

pip install torch

pip install python-dotenv

pip install certifi

NOTE: If you get a problem when running pip install pyaudio run brew install portaudio.

Run python3 add_audio.py

Select the audio input by typing the relevant number and then press enter. Record each sound in sequence.

NOTE: If you get a problem when running python add_audio.py run brew install wget.

Go to MongoDB Atlas and create an Atlas Search Index in the audio database sounds collection and using the content of searchindex.json

{
    "mappings": {
      "dynamic": true,
      "fields": {
        "emb": {
          "dimensions": 2048,
          "similarity": "cosine",
          "type": "knnVector"
        }
      }
    }
  }

Run python3 live_query.py and place your microphone next to the fan.

Switch to a new console and cd to the 'nodeUI' directory.

Run npm install

Run node nodeui.js

Use a browser to open the link http://localhost:3000/

In Atlas, go to charts and click the down arrow next to Add Dashboard then click import dashboard.

Select the file Sounds.charts and click next.

Click on the pencil icon and ensure the database and collection match audio and results.

Click 'Save', and the 'Save'.

Click the new dashboard 'Sounds' to see analytics on the sounds that are being detected by the microphone.