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) |
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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 |
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Country | United States |
City | San Jose |
Period | 12/11/16 → 12/13/16 |
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