| language: | |
| - en | |
| license: mit | |
| tags: | |
| - audio | |
| - bioacoustics | |
| - environmental-sound-classification | |
| - tinyml | |
| - tensorflow | |
| - tflite | |
| - raspberry-pi-pico | |
| - edge-ai | |
| - ecopulse | |
| datasets: | |
| - ESC-50 | |
| - FSD50K | |
| - InsectSet32 | |
| - HuggingFace-bird-local | |
| library_name: tensorflow | |
| pipeline_tag: audio-classification | |
| thumbnail: https://huggingface.co/front/assets/huggingface_logo.svg | |
| ## Resume | |
| Tiny audio classifier designed for environmental monitoring on ultra-low-power microcontrollers such as the Raspberry Pi Pico 2 W. The model classifies environmental sounds into six coarse classes and is optimized for TinyML deployment. | |
| ## Specifications | |
| Task: bioacoustic coarse classification | |
| Classes: bird, human, motor, rain_wind, insect, animal | |
| Input: log-mel spectrogram 149x40x1 | |
| Model size: 73.6 KB INT8 | |
| Target: Raspberry Pi Pico 2 W / TinyML | |
| Test accuracy: 87.44 % | |
| Datasets: ESC-50, FSD50K, InsectSet32, filtered bird dataset | |
| Limitations: modèle expérimental, pas encore validé terrain | |
| ## Usage | |
| ```py | |
| import tensorflow as tf | |
| model = tf.keras.models.load_model("ecopulse_cnn.keras") | |
| ``` |