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 language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 249-259 |
| Number of pages | 11 |
| Volume | 6098 LNAI |
| Edition | PART 3 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010 - Cordoba Duration: 2010 Jun 1 → 2010 Jun 4 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 3 |
| Volume | 6098 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010 |
|---|---|
| City | Cordoba |
| Period | 10/6/1 → 10/6/4 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Violin fingering estimation based on violin pedagogical fingering model constrained by bowed sequence estimation from audio input
AU - Maezawa, Akira
AU - Itoyama, Katsutoshi
AU - Takahashi, Toru
AU - Komatani, Kazunori
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
PY - 2010
Y1 - 2010
N2 - 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).
AB - 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).
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U2 - 10.1007/978-3-642-13033-5_26
DO - 10.1007/978-3-642-13033-5_26
M3 - Conference contribution
AN - SCOPUS:79551512155
SN - 3642130321
SN - 9783642130328
VL - 6098 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 249
EP - 259
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -