Improving piano music transcription by elman dynamic neural networks

In this paper, we present two methods based on neural networks for the automatic transcription of polyphonic piano music. The input to these methods consists in live piano music acquired by a microphone, while the pitch of all the notes in the corresponding score forms the output. The aim of this work is to compare the accuracy achieved using a feed-forward neural network, such as the MLP (MultiLayer Perceptron), with that supplied by a recurrent neural network, such as the ENN (Elman Neural Network). Signal processing techniques based on the CQT (Constant-Q Transform) are used in order to create a time-frequency representation of the input signals. The processing phases involve non-negative matrix factorization (NMF) for onset detection. Since large scale tests were required, the whole process (synthesis of audio data generated starting from MIDI files, comparison of the results with the original score) has been automated. Test, validation and training sets have been generated with reference to three different musical styles respectively represented by J. S. Bach’s inventions, F. Chopin’s nocturnes and C. Debussy’s preludes.