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

Authors: Pati, Kumar Ashis; Lerch, Alexander
Affiliation: Georgia Institute of Technology, Atlanta, GA, USA
AES Conference: 2017 AES International Conference on Semantic Audio (June 2017)
Paper Number: P2-3
Publication Date: June 13, 2017 Import into BibTeX
Subject: Semantic Audio
Permalink: http://www.aes.org/e-lib/browse.cfm?elib=18773

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