Piano is one of the most popular music instruments. During the piano performance, loudness is an important factor
for expressiveness, alongside tempo, changes in dynamics play with expectation, convey various emotions, and
render expressiveness. Due to the polyphonic characteristics and with the goal of better analysing the expressiveness of performance of piano with multiple notes playing
simultaneously, it is more useful to find loudness for each
note than looking at accumulated loudness for ...
Piano is one of the most popular music instruments. During the piano performance, loudness is an important factor
for expressiveness, alongside tempo, changes in dynamics play with expectation, convey various emotions, and
render expressiveness. Due to the polyphonic characteristics and with the goal of better analysing the expressiveness of performance of piano with multiple notes playing
simultaneously, it is more useful to find loudness for each
note than looking at accumulated loudness for a single time
frame. Most of the research in this topic uses Non-negative
Matrix Factorization (NMF) techniques to find note level
loudness. In contrast, we propose to use Deep Neural Networks (DNNs) conditioned with score information to estimate the loudness based on MIDI velocity for each note
performed by piano players. To our best knowledge, this
is a novel research for note level MIDI velocity estimation
by a DNN model in end to end fashion having FiLM conditioning.
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