A Dataset and Method for Guitar Solo Detection in Rock Music

Pati KA, Lerch A. A Dataset and Method for Guitar Solo Detection in Rock Music. In: AES Conference: 2017 AES International Conference on Semantic Audio. Audio Engineering Society; 2017. Paper P2-3. Available from: https://aes.org/publications/elibrary-page/?id=18773

@inproceedings{Pati2017_18773,
  author = {Pati, Kumar Ashis and Lerch, Alexander},
  title = {{A Dataset and Method for Guitar Solo Detection in Rock Music}},
  booktitle = {AES Conference: 2017 AES International Conference on Semantic Audio},
  note = {Paper P2-3},
  year = {2017},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18773}
}

TY  - CPAPER
TI  - A Dataset and Method for Guitar Solo Detection in Rock Music
AU  - Pati, Kumar Ashis
AU  - Lerch, Alexander
T2  - AES Conference: 2017 AES International Conference on Semantic Audio
M1  - Paper P2-3
PY  - 2017
DA  - 2017/06/06
UR  - https://aes.org/publications/elibrary-page/?id=18773
PB  - Audio Engineering Society
LA  - en
AB  - This paper explores the problem of automatically detecting electric guitar solos in rock music. A baseline study using standard spectral and temporal audio features in conjunction with an SVM classifier is carried out. To improve detection rates, custom features based on predominant pitch and structural segmentation of songs are designed and investigated. The evaluation of different feature combinations suggests that the combination of all features followed by a post-processing step results in the best accuracy. A macro-accuracy of 78.6% with a solo detection precision of 63.3% is observed for the best feature combination. This publication is accompanied by release of an annotated dataset of electric guitar solos to encourage future research in this area
ER  -