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Introduction to CNNs (Speech Stamps)


Sam Witteveen and I started the TensorFlow and Deep Learning Singapore group on MeetUp back in February 2017, and the group has grown significantly over the last ~12 months. However, that also means that many of the people who have recently joined missed out on our early sessions. So we're re-running some of the earlier events as "Replay" "Back-to-Basics" events, hoping that new people will be able to 'catch up' and get more out of coming to the main graoup events.

Our second 'Back-to-Basics' Meeting was supported by SGInnovate, in their office on Carpenter St.

As in the original March talk last year, I presented an introduction to CNNs, which are typically presented as a vision solution, using MNIST as an example. However, my version has a bit of a twist : Instead of using visual digits, I have created a spoken-word dataset (the digits 0 to 9, of course), and the CNN is trained to recognise spectrograms of the audio - i.e. the CNN is doing voice recognition!

As an added bonus, there's also an 'animals' audio dataset, and the demo notebook includes an illustration of transfer learning : Where animal names are learned solely by modeling the errors made by the network trained to recognise digits. Fortunately, during the demo, the transfer-learned model scored 4/4 on the test animal name examples (YMMV).

The source for the CNN 'Stamps' Speech Recognition model is available on GitHub - if you have questions on the software, please leave an 'issue' there.

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If there are any questions about the presentation please ask below, or contact me using the details given on the slides themselves.

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