Context-aware Automatic Music Transcription
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This paper presents an Automatic Music Transcription system that incorporates context-related information. Motivated by the state-of-art psychological research, we propose a methodology boosting the accuracy of AMT systems by modeling the adaptations that performers apply to successfully convey their interpretation in any acoustical context. In this work, we show that exploiting the knowledge of the source acoustical context allows reducing the error related to the inference of MIDI velocity. The proposed model structure first extracts the interpretation features and then applies the modeled performer adaptations. Interestingly, such a methodology is extensible in a straightforward way since only slight efforts are required to train completely context-aware AMT models.
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