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Build sound classification models for mobile apps with Teachable Machine and TFLite

↗ Quelle (blog.tensorflow.org)
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blog.tensorflow.org

Posted by , or now allows you to train your own sound classification model and export it in the TensorFlow Lite (TFLite) format. Then you can integrate the TFLite model to your mobile applications or your IoT devices. This is an easy way to quickly get up and running with sound classification, and you can then explore building production models in Python and exporting them to TFLite as a next step.

Model architecture

(2D time-frequency representation of sound obtained through Fourier transform). It first processes the spectrogram with successive layers of 2D convolution (Conv2D) and max pooling layers. The model ends in a number of dense (fully-connected) layers, which are interleaved with dropout layers for the purpose of reducing overfitting during training. The final output of the model is an array of probability scores, one for each class of sound the model is trained to recognize.

You can find a tutorial to train your own sound classifications models using this approach in Python website

  • Create an audio project
  • Record some sound clips for each category that you want to recognize. You need only 8 seconds of sound for each category.
  • Start training. Once it has finished, you can test your model on live audio feed.
  • Export the model in TFLite format.
  • (which includes steps to convert the model to TFLite format) to train your sound classification. Google Colab offers a free GPU so that you can significantly speed up your model training.

    Deploy the model to Android with TensorFlow Lite

    Once you have trained your TensorFlow Lite sound classification model, you can just put it in this Android sample app to try it out. Just follow these steps:

    1. Clone the sample app from GitHub:
      git clone https://github.com/tensorflow/examples.git
    2. Import the sound classification Android app into Android Studio. You can find it in the lite/examples/sound_classification/android folder.
    3. Add your model (both the soundclassifier.tflite and labels.txt) into the src/main/assets folder replacing the example model that is already there.
    4. Build the app and deploy it on an Android device. Now you can classify sound in real time!
    to allow easy training of sound classification in Python. Stay tuned!

    Acknowledgements

    This project is a joint effort between multiple teams inside Google. Special thanks to:

    • Google Research: Shanqing Cai, Lisie Lillianfeld
    • TensorFlow team: Tian Lin
    • Teachable Machine team: Gautam Bose, Jonas Jongejan
    • Android team: Saryong Kang, Daniel Galpin, Jean-Michel Trivi, Don Turner
    Vollständiger Original-Bericht
    Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
    ↗ Original-Artikel auf blog.tensorflow.org lesen
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