Abstract
Keys provide musical context and key modulation (changes) forms a crucial feature of music. In the age of big music data collections, automatic key segmentation is an important step towards music indexing and structure analysis. When using template-based key-finding methods, the best segmentation must minimize intra-segment distance to keys while maximizing inter-segment distance for neighboring keys. We present a general dynamic programming (DP) solution to this segmentation problem that is applicable to all distance-based key-finding methods and that does not require the number of segments to be pre-defined. This metaalgorithm is applied to the Kostka-Payne and Beatles datasets with three widely used distance-based key-finding methods. The key-finding results are evaluated using a compound score, and precision and recall. Statistical analysis of the results show that a precision value of 0.9 can be achieved with both datasets; for excerpts in one key, an average compound score above 0.8 is reported.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 603-608 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509045709 |
| DOIs | |
| State | Published - Jan 18 2017 |
| Externally published | Yes |
| Event | 18th IEEE International Symposium on Multimedia, ISM 2016 - San Jose, United States Duration: Dec 11 2016 → Dec 13 2016 |
Other
| Other | 18th IEEE International Symposium on Multimedia, ISM 2016 |
|---|---|
| Country | United States |
| City | San Jose |
| Period | 12/11/16 → 12/13/16 |
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Keywords
- Correlation
- Dynamic programming
- Euclidean distance
- Key segmentation
- Kullback-Leibler divergence
- Tonality
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Media Technology
- Computer Science Applications
Cite this
An optimization-based approach to key segmentation. / Chuan, Ching-Hua; Chew, Elaine.
Proceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 603-608 7823697.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An optimization-based approach to key segmentation
AU - Chuan, Ching-Hua
AU - Chew, Elaine
PY - 2017/1/18
Y1 - 2017/1/18
N2 - Keys provide musical context and key modulation (changes) forms a crucial feature of music. In the age of big music data collections, automatic key segmentation is an important step towards music indexing and structure analysis. When using template-based key-finding methods, the best segmentation must minimize intra-segment distance to keys while maximizing inter-segment distance for neighboring keys. We present a general dynamic programming (DP) solution to this segmentation problem that is applicable to all distance-based key-finding methods and that does not require the number of segments to be pre-defined. This metaalgorithm is applied to the Kostka-Payne and Beatles datasets with three widely used distance-based key-finding methods. The key-finding results are evaluated using a compound score, and precision and recall. Statistical analysis of the results show that a precision value of 0.9 can be achieved with both datasets; for excerpts in one key, an average compound score above 0.8 is reported.
AB - Keys provide musical context and key modulation (changes) forms a crucial feature of music. In the age of big music data collections, automatic key segmentation is an important step towards music indexing and structure analysis. When using template-based key-finding methods, the best segmentation must minimize intra-segment distance to keys while maximizing inter-segment distance for neighboring keys. We present a general dynamic programming (DP) solution to this segmentation problem that is applicable to all distance-based key-finding methods and that does not require the number of segments to be pre-defined. This metaalgorithm is applied to the Kostka-Payne and Beatles datasets with three widely used distance-based key-finding methods. The key-finding results are evaluated using a compound score, and precision and recall. Statistical analysis of the results show that a precision value of 0.9 can be achieved with both datasets; for excerpts in one key, an average compound score above 0.8 is reported.
KW - Correlation
KW - Dynamic programming
KW - Euclidean distance
KW - Key segmentation
KW - Kullback-Leibler divergence
KW - Tonality
UR - http://www.scopus.com/inward/record.url?scp=85015207543&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015207543&partnerID=8YFLogxK
U2 - 10.1109/ISM.2016.49
DO - 10.1109/ISM.2016.49
M3 - Conference contribution
AN - SCOPUS:85015207543
SP - 603
EP - 608
BT - Proceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -