Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input

Akira Maezawa, Katsutoshi Itoyama, Toru Takahashi, Kazunori Komatani, Tetsuya Ogata, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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% → 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).

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages249-259
Number of pages11
Volume6098 LNAI
EditionPART 3
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010 - Cordoba
Duration: 2010 Jun 12010 Jun 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6098 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010
CityCordoba
Period10/6/110/6/4

Fingerprint

Audio recordings
Strings
Normalization
Estimator
Subjective Evaluation
Error correction
Error Correction
Estimation Error
Model
Error analysis
High Accuracy
Acoustic waves
Students
Robustness
Sound

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Maezawa, A., Itoyama, K., Takahashi, T., Komatani, K., Ogata, T., & Okuno, H. G. (2010). Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 6098 LNAI, pp. 249-259). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6098 LNAI, No. PART 3). https://doi.org/10.1007/978-3-642-13033-5_26

Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input. / Maezawa, Akira; Itoyama, Katsutoshi; Takahashi, Toru; Komatani, Kazunori; Ogata, Tetsuya; Okuno, Hiroshi G.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6098 LNAI PART 3. ed. 2010. p. 249-259 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6098 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Maezawa, A, Itoyama, K, Takahashi, T, Komatani, K, Ogata, T & Okuno, HG 2010, Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 6098 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6098 LNAI, pp. 249-259, 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010, Cordoba, 10/6/1. https://doi.org/10.1007/978-3-642-13033-5_26
Maezawa A, Itoyama K, Takahashi T, Komatani K, Ogata T, Okuno HG. Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 6098 LNAI. 2010. p. 249-259. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-13033-5_26
Maezawa, Akira ; Itoyama, Katsutoshi ; Takahashi, Toru ; Komatani, Kazunori ; Ogata, Tetsuya ; Okuno, Hiroshi G. / Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6098 LNAI PART 3. ed. 2010. pp. 249-259 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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