Anshuman Fauzdar / Predictive_Audio Public

Anshuman Fauzdar / Predictive_Audio

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

About this project

Important part of predictive maintenance includes Audio that is microphone data from devices:

Here we have 3 types of data required to clasify and predict fan noise of 3D printer [Nearest industrial machine available right now]


background background-frequency

Fan Noise

fanNoise fanNoise-frequency

Fan Disturbance

fanDisturbance fanDisturbance-frequency

Type Time [m, s]
background 6m 40s
fanNoise 9m 10s
fanDisturbance 6m 40s
Total Dataset 22m 30s

Microphone Used:




Creating your first impulse (100% complete)

Acquire data

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

Title Type Size
MFE training data NPY file 1350 windows
MFE training labels NPY file 1350 windows
MFE testing data NPY file 350 windows
MFE testing labels NPY file 350 windows
NN Classifier model TensorFlow Lite (float32) 15 KB
NN Classifier model TensorFlow Lite (int8 quantized) 9 KB
NN Classifier model TensorFlow SavedModel 27 KB

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Data collected
28m 21s

Project info

Project ID 86908
Project version 1