https://github.com/aureleq/honkdetector
Car honk detector using Edge Impulse
https://github.com/aureleq/honkdetector
dataset edge-ai edge-impulse honk-detector machine-learning urbansound8k
Last synced: 7 months ago
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Car honk detector using Edge Impulse
- Host: GitHub
- URL: https://github.com/aureleq/honkdetector
- Owner: aureleq
- Created: 2020-02-26T09:18:12.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-09T20:50:58.000Z (over 5 years ago)
- Last Synced: 2025-01-21T16:15:01.448Z (9 months ago)
- Topics: dataset, edge-ai, edge-impulse, honk-detector, machine-learning, urbansound8k
- Language: Python
- Size: 8.79 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Honk Detector
The aim of this project is to be able to detect sounds made by a car horn using Edge Impulse embedded machine learning solution.
We are using the [UrbanSound8K](https://urbansounddataset.weebly.com/) dataset to train our neural network.
## Requirements
You will need to create an account on [EdgeImpulse](edgeimpulse.com) first. It is also recommended to follow their tutorial on [sound recognition](https://docs.edgeimpulse.com/docs/audio-classification) to start with.
The project is based on [ST Discovery kit IOT node](https://www.st.com/en/evaluation-tools/b-l475e-iot01a.html) but it should work with any other supported hardware.
## Setup
- Download the UrbanSound8K following [this link](https://zenodo.org/record/1203745) and unzip its content
- Rename file `credentials.json.template` to `credentials.json`
- Update the json file with your EdgeImpulse credentials (api key, hmac key, device name)
- Install python `librosa` and `requests` packages
## Uploading wav files to ingestion service
The script `wavToEdgeImpulse.py` imports wav files from UrbanSound8K dataset and convert files to mono/16kHz format. They are uploaded to EdgeImpulse ingestion service in both training and testing datasets.
Some important variables and functions in the script:
- TESTING_SPLIT: part of files to consider as testing dataset. By default set to 25% (75% for training set)
- getWaveFiles(LABEL, 600, 0.5): by default we import 600 seconds of data , from which 50% is consider foreground sound.
The script may run for a couple of minutes as a few hundreds samples have to be converted and uploaded to the ingestion service. All samples will then appear in your Edge Impulse dashboard.
## TODO
- Import some reference background noise or additional UrbanSound8K labels to complete neural network training
- Add field results
- Adapt default firmware to continously monitor sound from ST IOT kit
## References
- J. Salamon, C. Jacoby and J. P. Bello, "[A Dataset and Taxonomy for Urban Sound Research](http://www.justinsalamon.com/uploads/4/3/9/4/4394963/salamon_urbansound_acmmm14.pdf)", 22nd ACM International Conference on Multimedia, Orlando USA, Nov. 2014.
- Gideon Mendels, [How to apply machine learning and deep learning methods to audio analysis](https://towardsdatascience.com/how-to-apply-machine-learning-and-deep-learning-methods-to-audio-analysis-615e286fcbbc)