It is a well known fact that the dynamics in piano performance gives significant effect in expressiveness. Taking the polyphonic nature of the instrument into account, analysing information to form dynamics for each performed note has significant meaning to understand piano performance in a quantitative way. It is also a key element in an education context for piano learners.
In this study, we developed a model for estimating MIDI velocity for each note, as one of indicators to represent loudness, ...
It is a well known fact that the dynamics in piano performance gives significant effect in expressiveness. Taking the polyphonic nature of the instrument into account, analysing information to form dynamics for each performed note has significant meaning to understand piano performance in a quantitative way. It is also a key element in an education context for piano learners.
In this study, we developed a model for estimating MIDI velocity for each note, as one of indicators to represent loudness, with a condition of score assuming educational use case, by a Deep Neural Network (DNN) utilizing a U-Net with Scaled Dot-Product Attention (Attention) and Feature-wise Linear Modulation (FiLM) conditioning.
As a result, we prove that effectiveness of Attention and FiLM conditioning, improved estimation accuracy and achieved the best result among previous researches using DNNs and showed its robustness across the various domain of test data.
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