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This paper presents an 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 new technique for detecting onsets in a piano performance. The technique is based on a combination of a bank of auditory filters, a network of integrate-and-fire neurons and a multilayer perceptron. Such a structure introduces several advantages over the standard peak-picking onset detection approach 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.