Violin Fingering Estimation According to Skill Level based on Hidden Markov Model

This paper describes a method that estimates the appropriate violin fingering pattern according to the player’s skill level. A violin can produce the same pitch for different fingering patterns, which generally vary depending on skill level. Our proposed method translates musical scores into suitable fingering patterns for the desired skill level by modeling a violin player’s left hand based on a hidden Markov model. In this model, fingering is regarded as the hidden state and the output is the musical note in the score. We consider that differences in fingering patterns depend on skill level, which determines the prioritization between ease of playing and performance expression, and this priority is related to the output probability. Transition probability is defined by the appropriateness and ease of the transitions between states in the musical composition. Manually setting optimal model parameters for these probabilities is difficult because they are too numerous. Therefore, we decide on the parameters by training with textbook fingering. Experimental results show that fingering can be estimated for a skill level using the proposed method. The results of evaluations conducted of the method’s fingering patterns for beginners indicate that they are as good as or better than textbook fingering patterns.