id: 05866596
dt: a
an: 05866596
au: Maezawa, Akira; Itoyama, Katsutoshi; Takahashi, Toru; Komatani, Kazunori;
    Ogata, Tetsuya; Okuno, Hiroshi G.
ti: Violin fingering estimation based on violin pedagogical fingering model
    constrained by bowed sequence estimation from audio input.
so: García-Pedrajas, Nicolás (ed.) et al., Trends in applied intelligent
    systems. 23rd international conference on industrial engineering and
    other applications of applied intelligent systems, IEA/AIE 2010,
    Cordoba, Spain, June 1‒4, 2010. Proceedings, Part III. Berlin:
    Springer (ISBN 978-3-642-13032-8/pbk). Lecture Notes in Computer
    Science 6098. Lecture Notes in Artificial Intelligence, 249-259 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-13033-5_26
ab: Summary: This work presents an automated violin fingering estimation method
    that facilitates a student violinist acquire the “sound” of his/her
    favorite recording artist created by the artist’s unique fingering.
    Our method realizes this by analyzing an audio recording played by the
    artist, and recuperating the most playable fingering that recreates the
    aural characteristics of the recording. Recovering the aural
    characteristics requires the bowed string estimation of an audio
    recording, and using the estimated result for optimal fingering
    decision. The former requires high accuracy and robustness against the
    use of different violins or brand of strings; and the latter needs to
    create a natural fingering for the violinist. We solve the first
    problem by detecting estimation errors using rule-based algorithms, and
    by adapting the estimator to the recording based on mean normalization.
    We solve the second problem by incorporating, in addition to generic
    stringed-instrument model used in existing studies, a fingering model
    that is based on pedagogical practices of violin playing, defined on a
    sequence of two or three notes. The accuracy of the bowed string
    estimator improved by 21 points in a realistic situation ($38\%
    \rightarrow 59\%$) by incorporating error correction and mean
    normalization. Subjective evaluation of the optimal fingering decision
    algorithm by seven violinists on 22 musical excerpts showed that
    compared to the model used in existing studies, our proposed model was
    preferred over existing one ($p = 0.01$), but no significant preference
    towards proposed method defined on sequence of two notes versus three
    notes was observed ($p = 0.05$).
rv: