The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS
Transactions ID Number: 27-568
Full Name: Daniele Casali
Position: Doctor (Researcher)
Age: ON
Sex: Male
Address: Via del Politecnico, 1
Country: ITALY
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E-mail address: daniele.casali@uniroma2.it
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Title of the Paper: Polyphonic music transcription by means of a dynamic classifier
Authors as they appear in the Paper: Giovanni Costantini, Massimiliano Todisco, Massimo Carota, Daniele Casali
Email addresses of all the authors: costantini@uniroma2.it,massimiliano.todisco@uniroma2.it, massimo.carota@uiroma2.it, daniele.casali@uniroma2.it
Number of paper pages: 10
Abstract: Two methods for automatic transcription of polyphonic piano music are presented in this paper, both of them makes use of neural networks. The input to these methods consists in piano music recordings stored in WAV files, 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. 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 respective!
ly represented by J.S Bach's inventions, F. Chopin's nocturnes and C. Debussy's preludes
Keywords: Automatic piano music transcription, MultiLayer Perceptron, Elman Neural Network, Constant-Q Transform
EXTENSION of the file: .doc
Special (Invited) Session: Static and Dynamic Classification Methods for Polyphonic Transcription of Piano Pieces in Different Musical Styles
Organizer of the Session: 591-555
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