  Note-level automatic music transcription is one of the most representative
music information retrieval (MIR) tasks and has been studied for various
instruments to understand music. However, due to the lack of high-quality
labeled data, transcription of many instruments is still a challenging task. In
particular, in the case of singing, it is difficult to find accurate notes due
to its expressiveness in pitch, timbre, and dynamics. In this paper, we propose
a method of finding note onsets of singing voice more accurately by leveraging
the linguistic characteristics of singing, which are not seen in other
instruments. The proposed model uses mel-scaled spectrogram and phonetic
posteriorgram (PPG), a frame-wise likelihood of phoneme, as an input of the
onset detection network while PPG is generated by the pre-trained network with
singing and speech data. To verify how linguistic features affect onset
detection, we compare the evaluation results through the dataset with different
languages and divide onset types for detailed analysis. Our approach
substantially improves the performance of singing transcription and therefore
emphasizes the importance of linguistic features in singing analysis.
