This paper describes an algorithm, which performs monophonic music transcription. A pitch tracker calculates the fundamental frequency of the signal from the autocorrelation function. A continuity-restoration block takes the extracted pitch and determines the score corresponding to the original performance. The signal envelope analysis completes the transcription system, calculating attack-sustain-decay-release times, which improves the synthesis process. Attention is also paid to the extraction of timbre and wavetable synthesis.

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This paper presents a piano transcription system that transforms audio into MIDI format. Human knowledge and psychoacoustic models are implemented in a blackboard architecture, which allows the adding of knowledge with a top-down approach. The analysis is adapted to the information acquired. This technique is referred to as a prediction-driven approach, and it attempts to simulate the adaptation and prediction process taking place in human auditory perception. In this paper we describe the implementation of Polyphonic Note Recognition using a Fuzzy Inference System (FIS) as part of the Knowledge sources in a Blackboard system. The performance of the transcription system shows how polyphonic music transcription is still an unsolved problem, with a success of 45% according to the Dixon formula. However if we consider only the transcribed notes the success increases to 74%. Moreover, the results obtained in the paper presented in [1], show how the transcription can be used with success in a retrieval system, encouraging the authors to develop this technique for more accurate transcription results.

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