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Note-level automatic music transcription is one of the most representativemusic information retrieval (MIR) tasks and has been studied for variousinstruments to understand music. However, due to the lack of high-qualitylabeled data, transcription of many instruments is still a challenging task. Inparticular, in the case of singing, it is difficult to find accurate notes dueto its expressiveness in pitch, timbre, and dynamics. In this paper, we proposea method of finding note onsets of singing voice more accurately by leveragingthe linguistic characteristics of singing, which are not seen in otherinstruments. The proposed model uses mel-scaled spectrogram and phoneticposteriorgram (PPG), a frame-wise likelihood of phoneme, as an input of theonset detection network while PPG is generated by the pre-trained network withsinging and speech data. To verify how linguistic features affect onsetdetection, we compare the evaluation results through the dataset with differentlanguages and divide onset types for detailed analysis. Our approachsubstantially improves the performance of singing transcription and thereforeemphasizes the importance of linguistic features in singing analysis.

A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription