https://github.com/GianlucaPaolocci/Sound-classification-on-Raspberry-Pi-with-Tensorflow

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
https://github.com/GianlucaPaolocci/Sound-classification-on-Raspberry-Pi-with-Tensorflow

audio-analysis audio-signals dataset librosa machine-learning multilayer-perceptron-network raspberry raspberry-pi sound-classification tensorflow tensorflow-models

Last synced: 5 days ago
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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

• Host: GitHub
• URL: https://github.com/GianlucaPaolocci/Sound-classification-on-Raspberry-Pi-with-Tensorflow
• Owner: GianlucaPaolocci
• License: mit
• Created: 2017-06-20T12:08:31.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-11-16T09:49:26.000Z (almost 2 years ago)
• Last Synced: 2024-10-29T23:34:28.031Z (12 days ago)
• Topics: audio-analysis, audio-signals, dataset, librosa, machine-learning, multilayer-perceptron-network, raspberry, raspberry-pi, sound-classification, tensorflow, tensorflow-models
• Language: Python
• Homepage:
• Size: 385 KB
• Stars: 94
• Watchers: 9
• Forks: 30
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SOUND CLASSIFICATION WITH TENSORFLOW ON RASPBERRY PI

![alt text](https://raw.githubusercontent.com/GianlucaPaolocci/Sound-classification-on-Raspberry-Pi-with-Tensorflow/master/img/Immagine.png)

#  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

2. **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.

3. **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:

  - **[email protected]**