Edge Impulse Inc. / Tutorial: Recognize sounds from audio
This is the finished Edge Impulse project for the tutorial 'Recognize sounds from audio'. From here you acquire new training data, design impulses and train models.
About this project
Running Sink Faucet Detection
Have you ever wanted to know if you left an appliance running while you were away from home? Clone this project to build an embedded ML project detecting when your sink's faucet is still running with incoming audio data!
You can also follow our tutorial to guide you through building your continuous audio recognition model, from data collection to deployment on embedded devices.
Sensor & Block Information
- Microphone audio data (.wav files) @ 16000 Hz
- MFE DSP block for non-human voice audio
- Neural Network Classifier with prediction outputs: "faucet", "noise"
Creating your first impulse (100% complete)
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
Data collected15m 40s
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