Neural Networks for Note Onset Detection in Piano Music

This paper presents a brief overview of our researches in the use of connectionist systems for transcription of polyphonic piano music and concentrates on the issue of onset detection in musical signals. We propose a technique for detecting onsets in a piano performance, based on a combination of a bank of auditory filters, a network of integrate-and-fire neurons and a multilayer perceptron. Such structure has certain advantages over the more commonly used peak-picking methods and we present its performance on several synthesized and real piano recordings. Results show that our approach represents a viable alternative to existing onset detection algorithms.