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Applying Multilayer Perceptron Artificial Neural Networks to Recognizing Piano Keystrokes

Applying Multilayer Perceptron Artificial Neural Networks to Recognizing Piano Keystrokes. By: Josh Tabor. The Project. Create an MLP ANN to correctly identify which piano keys are pushed based on their FFT coefficients Test ANN at different noise levels and maybe on different pianos.

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Applying Multilayer Perceptron Artificial Neural Networks to Recognizing Piano Keystrokes

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  1. Applying Multilayer Perceptron Artificial Neural Networks to Recognizing Piano Keystrokes By: Josh Tabor

  2. The Project Create an MLP ANN to correctly identify which piano keys are pushed based on their FFT coefficients Test ANN at different noise levels and maybe on different pianos

  3. The Plan Collect data (Middle C – Tenor C) Keys to be used

  4. The Plan (continued) • Antialiasing Filter • Downsample • Sampled at 44.1Khz • Highest f= 523Hz • Downsample to 1200Hz • Saves processing time • Breakup signal

  5. The Plan (continued) Take FFT Average windows Label Develop ANN Test ANN

  6. Expected Results Expect it to work fairly well (90% classification rate) FFT cleaner than expected Performance degrades with SNR decrease

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