Ever struggled to validate or differentiate alert tones in a device you are testing? Need a way to confirm that a .wav file truly matches what’s specified in your alert documentation?
Below are the first steps, which will help you.
- Visualize audio waveforms for .wav files
- Confirm tone durations, spacing, and structure
- Compare alerts for uniqueness and clarity
- Set up the foundation for regression testing of audio alerts
We decided to make it fun. We plotted roja.wav – a music snippet from A.R. Rahman’s legendary BGM from the movie Roja.
Because if you are going to test audio tooling, why not start with a masterpiece?
What the Tool Does
This project reads .wav files using scipy, processes them using numpy, and plots the waveform using matplotlib. It is entirely local – no web server, no UI fuss – just fast, readable code that gives you a clear time-domain plot of your audio file.
Why This Matters for Clinical Alert Testing
Here is how this helps in serious testing scenarios:
1. Visual Validation of Alert Specs
- Is the alarm tone exactly 500 ms?
- Is there a required gap between pulses?
- Is the tone distinct from others?
With waveform visualization, you do not just hear it – you see it.
2. Alert Tone Comparison
Quickly compare two .wav files for overlap in structure. You’ll know if two alerts are visually (and audibly) too similar—before it becomes a safety risk.
3. Regression Checks
If your alert tones change across builds, this tool gives you a baseline for waveform comparison. Add automation, and you have got real audio regression testing.
4. Analyze Field Recordings
Got an alert captured from a real device in the lab? Drop it in, and you can visually inspect if the playback matches the expected waveform.
Project Overview
Directory Structure
audio-waveform_automated-testing/
├── data/ # Audio files (.wav)
├── src/
│ └── waveform_plotter.py # Core logic for plotting
├── main.py # Entry point
├── requirements.txt # Dependencies
└── .gitignoreTechnical Details
- numpy: For audio data manipulation
- scipy.io.wavfile: For reading .wav files
- matplotlib: For plotting amplitude vs. time
Installation
pip install -r requirements.txtHighlights:
- Stereo-to-mono fallback for 2-channel files
- Auto time-axis generation using sample rate
- Simple CLI-style main.py launcher
About That BGM…
Yes, the first waveform we plotted was roja.wav. Seeing A.R. Rahman’s iconic beats take shape as amplitude curves on a graph was oddly satisfying. It proved the tool worked – and gave it style.
What’s Next
- Add FFT for frequency domain analysis
- Auto-detect alert duration and silence gaps
- Build a lightweight web UI using Streamlit
GitHub Repo
Check it out here: https://github.com/stonetusker/audio-waveform-automated-testing
Final Thoughts
Testing audio alerts is not just about what we hear – it is about what we can measure and visualize. This project started as a utility but quickly became a bridge between auditory design and objective QA. Whether it’s Rahman’s Roja or a critical ventilator alarm, every waveform tells a story. For test engineers, tools like this bring us one step closer to safer, smarter systems.
