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KU. AS. Pati, and A. Lerch, "A Dataset and Method for Guitar Solo Detection in Rock Music," Paper P2-3, (2017 June.). doi: KU. AS. Pati, and A. Lerch, "A Dataset and Method for Guitar Solo Detection in Rock Music," Paper P2-3, (2017 June.). doi:
Abstract: 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

@article{pati2017a,
author={pati, kumar ashis and lerch, alexander},
journal={journal of the audio engineering society},
title={a dataset and method for guitar solo detection in rock music},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},} @article{pati2017a,
author={pati, kumar ashis and lerch, alexander},
journal={journal of the audio engineering society},
title={a dataset and method for guitar solo detection in rock music},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},
abstract={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},}

TY - paper
TI - A Dataset and Method for Guitar Solo Detection in Rock Music
SP - EP -
AU - Pati, Kumar Ashis
AU - Lerch, Alexander
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017 TY - paper
TI - A Dataset and Method for Guitar Solo Detection in Rock Music
SP - EP -
AU - Pati, Kumar Ashis
AU - Lerch, Alexander
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017
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