In this project is presented a simple method to train an MLP neural network for audio signals. The trained model can be exported on a Raspberry Pi (2 or superior suggested) to classify audio signal registered with USB microphone
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README.md

SOUND CLASSIFICATION WITH TENSORFLOW ON RASPBERRY PI

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BUILD THE PROJECT

The project is developed and tested with Python 2.7.

Install following Python libraries on your PC/Workstation and Raspberry Pi:

Tensorflow, Scikit-learn, Librosa

Install following library on your Raspberry only:

Sounddevice
  1. DOWNLOAD UrbanSound8K DATASET

https://serv.cusp.nyu.edu/projects/urbansounddataset/urbansound8k.html

  1. TRAIN THE MODEL

Set the right path where you downloaded the dataset in your code.

Set the right path where you want to save the trained model.

Run "trainModel.py" on your PC/Workstation.

  1. RUN THE MODEL

Export the trained model on you Raspberry Pi ('model.meta', 'model.index', 'checkpoint', 'model.data-00000-of-00001').

Export 'fit_params.npy' on your Raspberry Pi.

Run "classiPi.py" on your Raspberry and enjoy!

REMEMBER TO

Remember to reference this project in your works.

AUTHORS

Gianluca Paolocci, University of Naples Parthenope, Science and Techonlogies Departement, Ms.c Applied Computer Science https://www.linkedin.com/in/gianluca-paolocci-a19678b6/

Luigi Russo, University of Naples Parthenope, Science and Techonlogies Departement, Ms.c Applied Computer Science

CONTACTS

if you have problems, questions, ideas or suggestions, please contact me to: