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This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
Performance Calibration: Bird sound classifier
Classifies audio as representative of either the house sparrow, rose-ringed parakeet, or background noise.
About this project
Have you ever wondered how to use your Edge Impulse project's Performance calibration feature to optimize your audio detection models? Performance calibration allows you to test, fine-tune, and simulate running your model with continuous real-world or synthetically generated audio data streams to gain an immediate understanding of how your model will perform in the field. Clone this project to build an embedded ML project to detect various bird calls in your environment from your device's microphone input!
Sensor & Block Information
- Microphone audio data (.wav files) @ 16000Hz
- MFCC DSP block for non-human voice audio
- Neural Network Classifier with prediction outputs: "housesparrow", "roseringedparakeet", "noise"
Run this model
On any device
Dataset summary
Data collected
2h 33m 47sSensor
audio @ 16KHzLabels
housesparrow, noise, roseringedparakeetProject info
| Project ID | 16060 |
| Project version | 3 |
| License | BSD 3-Clause Clear |
| No. of views | 427,025 |
| No. of clones | 114 |
