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I would like to write a Python program that takes an audio file as input, recognizes clapping sounds in it, then extracts these sounds into a file.

My idea is the following:

  1. Get (or create) a dataset containing many audio clips with clapping sounds in them,
  2. Train a machine learning model to recognize these sounds,
  3. Test the model on new audio files.

How can I achieve point 2? I already have a dataset ready, so I just need to run a ML algorithm on them. I've seen several alternatives:

  1. Keras
  2. PyTorch
  3. Scikit-learn

But I have no idea which to choose for this particular task and how I would go about writing a Python script that does the training and testing. Can someone please help?

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Claps have specific waves that could be detected with several solutions, including ML.

Here is a code using keras:

https://www.kaggle.com/code/carlolepelaars/bidirectional-lstm-for-audio-labeling-with-keras

In addition to that, there is an eating sound project that is quite similar and there is probably useful code like this one with wav2vec:

https://github.com/m3hrdadfi/soxan/blob/main/notebooks/Eating_Sound_Collection_using_Wav2Vec2.ipynb

On the other hand, non-ML could have even better results, as they follow simple rules that detect claps efficiently. For instance:

https://github.com/nikhiljohn10/pi-clap

https://github.com/xSparfuchs/clap-detection/blob/master/clap-detection.py

https://github.com/tom-s/clap-detector

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  • $\begingroup$ Thanks bro, 'preciate it $\endgroup$
    – Klangen
    Dec 21, 2022 at 12:06
  • $\begingroup$ You're welcome :) $\endgroup$ Dec 21, 2022 at 20:48

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