Note level midi velocity estimation for piano performance

Abstract

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.