A Comparison of Feed Forward Neural Network Architectures for Piano Music Transcription
Matija Marolt · University of Michigan Library Repository · 1999
This paper presents our experiences with the use of feed forward neural networks for piano chord recognition and polyphonic piano music transcription. Our final goal is to build a transcription system that would transcribe polyphonic piano music over the entire piano range. The central part of our system uses neural networks acting as pattern recognisers and extracting notes from the source audio signal. The paper presents results obtained by using several feed forward neural network architectures for transcription, namely multilayer perceptrons, RBF networks, support vector machines and time-delay networks.