These individual scores were measured at each musical note, regarding the pitch, onset, and offset accuracy. The technique is implemented using a Bayesian classifier, which is trained using an audio dataset containing individual scores provided by a committee of expert listeners. On the second stage, a statistical method is used to evaluate the accuracy of each detected sung note. This stage implicitly aggregates and links the best combination of the extracted melodic segments with the expected note in the ground truth. On the first stage, an aggregation process is introduced to perform the temporal alignment between the transcribed melody and the music score (ground truth). The proposed system uses melodic transcription techniques to extract the sung notes from the audio signal, and the sequence of melodic segments is subsequently processed by a two stage algorithm. This paper presents a note-by-note approach for automatic solfège assessment.
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