Edge Impulse Inc. / 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"
Download block output
| Title | Type | Size | |
|---|---|---|---|
| MFCC training data | NPY file | 4394 windows | |
| MFCC training labels | NPY file | 4394 windows | |
| MFCC testing data | NPY file | 10521 windows | |
| MFCC testing labels | NPY file | 10521 windows | |
| NN Classifier model | TensorFlow Lite (float32) | 13 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized) | 10 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized with float32 input and output) | 10 KB | |
| NN Classifier model | TensorFlow SavedModel | 23 KB |
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Summary
Data collected
2h 33m 47sProject info
| Project ID | 16060 |
| Project version | 3 |
| License | No license attached |
