Piano is one of the most popular instruments among people that learn to play music. When playing the piano, the
level of loudness is crucial for expressing emotions as well
as manipulating tempo. These elements convey the expressiveness of music performance. Detecting the loudness of
each note could provide more valuable feedback for music students, helping to improve their performance dynamics. This can be achieved by visualizing the loudness levels not only for self-learning purposes but also ...
Piano is one of the most popular instruments among people that learn to play music. When playing the piano, the
level of loudness is crucial for expressing emotions as well
as manipulating tempo. These elements convey the expressiveness of music performance. Detecting the loudness of
each note could provide more valuable feedback for music students, helping to improve their performance dynamics. This can be achieved by visualizing the loudness levels not only for self-learning purposes but also for effective communication between teachers and students. Also,
given the polyphonic nature of piano music, which often
involves parallel melodic streams, determining the loudness of each note is more informative than analyzing the
cumulative loudness of a specific time frame.
This research proposes a method using Deep Neural Network (DNN) with score information to estimate note-level
MIDI velocity of piano performances from audio input. In
addition, when score information is available, we condition the DNN with score information using a Feature-wise
Linear Modulation (FiLM) layer. To the best of our knowledge, this is the first attempt to estimate the MIDI velocity
using a neural network in an end to end fashion. The model
proposed in this study achieved improved accuracy in both
MIDI velocity estimation and estimation error deviation, as
well as higher recall accuracy for note classification when
compared to the DNN model that did not use score information
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