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Automatic music transcription is a challenging topic in audio signal processing. It consists in transforming the musical content of audio data into a symbolic notation. The system discussed in this paper takes as input the sound of a recorded polyphonic piano music and it produces conventional musical representation as output. For each note event two main characteristics are considered: the attack instant and the pitch. Onset detection is obtained through a time-frequency representation of the audio signal. Note classification is based on constant Q transform (CQT) and support vector machines (SVMs). In particular, in this paper we propose a short-term memory based feature vector for classification. To validate the efficacy of short-term memory, we present a collection of experiments using synthesized MIDI files and piano recordings, and we compare the results with other existing approaches.