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• W2090040637 abstract "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." @default.
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• W2090040637 date "2010-04-01" @default.
• W2090040637 modified "2023-09-23" @default.
• W2090040637 title "SVM based transcription system with short-term memory oriented to polyphonic piano music" @default.
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• W2090040637 doi "https://doi.org/10.1109/melcon.2010.5476305" @default.
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