Abstract
This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70% accuracy on average.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 408-417 |
| Number of pages | 10 |
| Volume | 8935 |
| ISBN (Print) | 9783319144443 |
| Publication status | Published - 2015 |
| Event | 21st International Conference on MultiMedia Modeling, MMM 2015 - Sydney Duration: 2015 Jan 5 → 2015 Jan 7 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8935 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 21st International Conference on MultiMedia Modeling, MMM 2015 |
|---|---|
| City | Sydney |
| Period | 15/1/5 → 15/1/7 |
Fingerprint
Keywords
- Audio scene detection
- Live version
- Music identification
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Live version identification with audio scene detection. / Ishikura, Kazumasa; Uemura, Aiko; Katto, Jiro.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8935 Springer Verlag, 2015. p. 408-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8935).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Live version identification with audio scene detection
AU - Ishikura, Kazumasa
AU - Uemura, Aiko
AU - Katto, Jiro
PY - 2015
Y1 - 2015
N2 - This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70% accuracy on average.
AB - This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70% accuracy on average.
KW - Audio scene detection
KW - Live version
KW - Music identification
UR - http://www.scopus.com/inward/record.url?scp=84919683746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919683746&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84919683746
SN - 9783319144443
VL - 8935
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 408
EP - 417
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -